Systems, methods and devices for monitoring betting activities

ABSTRACT

System, processes and devices for monitoring betting activities using bet recognition devices and a server. Each bet recognition device has an imaging component for capturing image data for a gaming table surface. The bet recognition device receives calibration data for calibrating the bet recognition device. A server processor coupled to a data store processes the image data received from the bet recognition devices over the network to detect, for each betting area, a number of chips and a final bet value for the chips.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/060,990, filed Oct. 1, 2020, which is a continuation of U.S.application Ser. No. 16/506,193, dated Jul. 9, 2019, which is acontinuation of U.S. application Ser. No. 16/150,686, dated 3 Oct. 2018,which is a divisional of U.S. application Ser. No. 15/309,102 (grantedas U.S. patent Ser. No. 10/096,206), dated 15 Apr. 2016, which is anational phase entry of PCT/CA2016/050442 dated 15 Apr. 2016, whichclaims all benefit, including priority of U.S. Application No.62/168,395, filed 29 May 2015, and U.S. Application No. 62/298,154,filed 22 Feb. 2016. All of these related applications are entitled“SYSTEMS, METHODS AND DEVICES FOR MONITORING BETTING ACTIVITIES”, andare incorporated herein by reference.

FIELD

Embodiments generally relate to the field of monitoring game activitiesat gaming tables in casinos and other gaming establishments, and inparticular, to monitoring game activities including betting activities.

INTRODUCTION

Casinos and gaming establishments may offer a variety of card games tocustomers. Card games involve various game activities, such as card playand betting, for example. A card game may be played at a gaming table byplayers, including a dealer and one or more customers. It may bedesirable for casinos or gaming establishments to monitor bettingactivities for security and management purposes.

Gaming establishments are diverse in layouts, lighting, and securitymeasures, among others. For example, betting markers, such as chips, mayhave varying designs and markings that not only distinguish between chiptypes (e.g., chip values), but also different series of chips having thesame values (e.g., to reduce the risk counterfeiting and/or to enabletracking).

SUMMARY

In an aspect, there is provided a system for monitoring game activitiesat a plurality of gaming tables comprising: a plurality of clienthardware devices for the plurality of gaming tables, each clienthardware device comprising an imaging component positioned on arespective gaming table or proximate thereto to capture image datacorresponding to the one or more chips positioned in a betting area on agaming surface of the respective gaming table and, in response,pre-processing the captured image data to generate a compressed set ofimage data free of background image data, each client hardware devicecomprising one or more sensors responsive to activation events anddeactivation events to trigger capture of the image data by the imagingcomponent; a game monitoring server for collecting, processing andaggregating the compressed image data from the client hardware devicesto generate aggregated betting data for the plurality of gaming tables;and a front end interface device for displaying the aggregated bettingdata from the game monitoring server for provision to or display on enduser systems, the front end interface device for receiving controlcommands from the end user systems for controlling the provision ordisplay of the aggregated betting data.

In another aspect, the imaging component is positioned to capture theimage data at an offset angle relative to a plane of the gaming surfaceof the respective gaming table; and wherein the offset angle permits theimaging component to capture the image data from sidewalls of the one ormore chips.

In another aspect, the offset angle is an angle selected from the groupof angles consisting of about −5 degrees, about −4 degrees, about −3degrees, about −2 degrees, about −1 degrees, about 0 degrees, about 1degrees, about 2 degrees, about 3 degrees, about 4 degrees, and about 5degrees; and the altitude is an altitude selected from the group ofaltitudes consisting of about 0.2 cm, about 0.3 cm, about 0.4 cm, about0.5 cm, about 0.6 cm, about 0.7 cm, about 0.8 cm, about 0.9 cm, andabout 1.0 cm.

In another aspect, the system further comprises an illumination stripadapted to provide a reference illumination on the one or more chips,the illumination strip positioned at a second substantially horizontalangle to provide illumination on the sidewalls of the one or more chips;the second substantially horizontal angle selected such that thepresence of shadows on the one or more chips is reduced.

In another aspect, the illumination strip is controllable by the clienthardware devices and configured to provide the reference illumination inaccordance with control signals received from the client hardwaredevices; the control signals, when processed by the illumination strip,cause the illumination strip to change an intensity of the referenceillumination based at least on ambient lighting conditions, the controlsignals adapted to implement a feedback loop wherein the referenceillumination on the one or more chips is substantially constant despitechanges to the ambient lighting conditions.

In another aspect, the one or more sensors are adapted to determine oneor more depth values corresponding to one or more distances from areference point to the one or more chips, each of the depth valuescorresponding to the distance to a corresponding chip.

In another aspect, the one or more sensors determine the one or moredepth values by using at least one of Doppler radar measurements,parallax measurements, infrared thermography, shadow measurements, lightintensity measurements, relative size measurements, and illuminationgrid measurements.

In another aspect, the one or more sensors include at least two sensorsconfigured to determine the one or more depth values by measuring stereoparallax.

In another aspect, at least one of the client hardware devices and thegame monitoring server are configured to determine a presence of one ormore obstructing objects that are partially or fully obstructing the oneor more chips from being sensed by the one or more sensors, the presenceof the one or more obstructing objects being determined by continuouslymonitoring the one or more depth values to track when the one or moredepth values abruptly changes responsive to the obstruction.

In another aspect, at least one of the client hardware devices and thegame monitoring server are configured to, responsive to positivelydetermining the presence of the one or more obstructing objects that arepartially or fully obstructing the one or more chips from being sensedby the one or more sensors, aggregate a plurality of captured imagesover a duration of time and to compare differences between each of theplurality of captured images to estimate the presence of the one or morechips despite the presence of the one or more obstructing objects thatare partially or fully obstructing the one or more chips from beingsensed by the one or more sensors.

In another aspect, the compressed set of image data free of backgroundimage data is obtained by using an estimated chip stack height incombination with the more one or more depth values to determine a chipstack bounding box that is used for differentiating between thebackground image data and chip image data during the pre-processing.

In another aspect, the game monitoring server is configured to processthe compressed set of image data free to individually identify one ormore specific chips of the one or more chips within the chip stackbounding box represented by the compressed set of image data, eachspecific chip being identified through a chip bounding box establishedaround the pixels representing the specific chip.

In another aspect, the game monitoring server is configured to identifyone or more chip values associated with each of the one or more chipswithin the chip stack bounding box by estimating a chip value based onmachine-vision interpretable features present on the one or more chips.

In another aspect, the game monitoring server is configured to identifythe one or more chip values by generating one or more histograms, eachof histogram corresponding with image data in the corresponding chipbounding box, by processing the one or more histograms to obtain one ormore waveforms, each waveform corresponding to a histogram; and the gamemonitoring server is configured to perform feature recognition on eachwaveform to compare each waveform against a library of pre-definedreference waveforms to estimate the one or more chip values throughidentifying the pre-defined reference waveform that has the greatestsimilarity to the waveform.

In another aspect, the processing of the one or more histograms toobtain the one or more waveforms includes at least performing a Fouriertransformation on the one or more histograms to obtain one or more plotsdecomposing each histogram into a series of periodic waveforms which inaggregation form the histogram.

In another aspect, the machine-vision interpretable features present onthe one or more chips include at least one of size, shape, pattern, andcolor.

In another aspect, the machine-vision interpretable features present onthe one or more chips include at least one of size, shape, pattern, andcolor and the one or more waveforms differ from one another at least dueto the presence of the machine-vision interpretable features.

In another aspect, the activation events and deactivation eventscomprising placement and removal of the one or more chips within a fieldof view of the imaging component.

In another aspect, the activation events and deactivation events aretriggered by a signal received from an external transmitter, theexternal transmitter being a transmitting device coupled to a dealershoe that transmits a signal whenever the dealer shoe is operated.

In another aspect, the system further includes an interface engineadapted to provision an interface providing real or near-real-timebetting data to a dealer, the real or near-real-time betting data basedon the betting data extracted by the game monitoring server from thecaptured image data, the betting data including one or more estimatedvalues for each stack of chips in one or more betting areas of thegaming surface.

In another aspect, there is provided a system for monitoring gameactivities comprising: a game monitoring server for collecting,processing and aggregating betting data from a plurality of clienthardware devices to generate aggregated betting data for a plurality ofgaming tables, each client hardware device having at least one imagingcomponent positioned substantially parallel to a gaming surface of arespective gaming table and configured to capture image datacorresponding to one or more chips positioned on the gaming surface inresponse to activation events, the betting data derived from the imagedata; and a front end interface device for displaying the aggregatedbetting data from the game monitoring server for provision to or displayon end user systems, the front end interface device for receivingcontrol commands from the end user systems for controlling the provisionor display of the aggregated betting data.

In another aspect, the imaging component is positioned to capture theimage data at an offset angle relative to a plane of the gaming surfaceof the respective gaming table; and wherein the offset angle permits theimaging component to capture the image data from sidewalls of the one ormore chips.

In another aspect, there is provided a device for monitoring gameactivities at a plurality of gaming tables comprising: an imagingcomponent positioned on a respective gaming table or proximate theretoto capture image data corresponding to the one or more chips positionedin a betting area on a gaming surface of the respective gaming tableand, in response, pre-processing the captured image data to generate acompressed set of image data free of background image data, each clienthardware device comprising one or more sensors responsive to activationevents and deactivation events to trigger capture of the image data bythe imaging component, the imaging component positioned substantiallyparallel to a gaming surface of the respective gaming table; a processorconfigured to pre-process the captured image data to generate acompressed set of image data free of background image data responsive toactivation events and deactivation events to trigger collection ofbetting events; and a communication link configured for transmitting thecompressed set of image data to a game monitoring server configured togenerate aggregated betting data for the plurality of gaming tables, thegenerated aggregated betting data being provided to a front endinterface device configured for displaying the aggregated betting datafrom the game monitoring server for provision to or display on end usersystems, the front end interface device configured for receiving controlcommands from the end user systems for controlling the provision ordisplay of the aggregated betting data.

In another aspect, there is provided a method for monitoring bettingactivities comprising: detecting, by an imaging component, that one ormore chips have been placed in one or more defined bet areas on a gamingsurface, each chip of the one or more chips having one or more visualidentifiers representative of a face value associated with the chip, theone or more chips arranged in one or more stacks of chips; capturing, bythe imaging component, image data corresponding to the one or more chipspositioned on the gaming surface, the capturing triggered by thedetection that the one or more chips have been placed in the one or moredefined bet areas; transforming, by an image processing engine, theimage data to generate a subset of the image data relating to the one ormore stacks of chips, the subset of image data isolating images of theone or more stacks from the image data; recognizing, by an imagerecognizer engine, the one or more chips composing the one or morestacks, the recognizer engine generating and associating metadatarepresentative of (i) a timestamp corresponding to when the image datawas obtained, (ii) one or more estimated position values associated withthe one or more chips, and (iii) one or more face values associated withthe one or more chips based on the presence of the one or more visualidentifiers; segmenting, by the image recognizer engine, the subset ofimage data and with the metadata representative of the one or moreestimated position values with the one or more chips to generate one ormore processed image segments, each processed image segmentcorresponding to a chip of the one or more chips and including metadataindicative of an estimated face value and position; and determining, bya game monitoring engine, one or more bet data values, each bet datavalue corresponding to a bet area of the one or more defined bet areas,and determined using at least the number of chips visible in each of theone or more bet areas extracted from the processed image segments andthe metadata indicative of the face value of the one or more chips.

In another aspect, the method further comprises transmitting, the one ormore bet data values corresponding to the one or more defined bet areas,to a gaming data repository, the game data repository configured forassociating the one or more bet data values to one or more bets made byone or more players as the one or more players interact with a gamebeing played on the gaming surface; and generating, on a display of acomputing device by n interface component, an electronic dashboardillustrative of at least one of current and historical bets made by theone or more players.

Many further features and combinations thereof concerning embodimentsdescribed herein will appear to those skilled in the art following areading of the instant disclosure.

DESCRIPTION OF THE FIGURES

In the figures:

FIGS. 1A and 1B illustrate a block diagrams of a system for monitoringbetting activities at gaming tables according to some embodiments.

FIG. 2 illustrates a block diagram of another system for monitoring gameactivities at gaming tables according to some embodiments.

FIG. 3 illustrates a block diagram of another system for monitoring gameactivities at gaming tables according to some embodiments.

FIGS. 4A-4C illustrates a schematic diagram of bet regions monitored bya bet recognition device according to some embodiments.

FIGS. 5 to 7 illustrate example images taken from a bet recognitiondevice mounted on a gaming table according to some embodiments.

FIGS. 8 and 9 illustrate example images of a bet recognition devicemounted on a gaming table according to some embodiments.

FIGS. 10 and 11 illustrate example images of a bet recognition deviceaccording to some embodiments.

FIG. 12 illustrate a schematic diagram of another example betrecognition device according to some embodiments.

FIGS. 13A, 13B and 14 illustrate example images from a bet recognitiondevice and processed images after transformation by server according tosome embodiments.

FIG. 15 illustrates a schematic diagram of a sensor array device for betrecognition device according to some embodiments.

FIG. 16 illustrates a schematic graph of the amplitude of the receivedsignal over time according to some embodiments.

FIG. 17 illustrates a schematic of a game monitoring server according tosome embodiments.

FIG. 18 illustrates a schematic of a bet recognition device according tosome embodiments.

FIGS. 19-23, 24A-24D, 25A-25E, 26 to 39 illustrate schematic diagrams ofbet recognition devices with camera layouts according to someembodiments.

FIGS. 40 to 43 illustrate schematic diagrams of shoe devices accordingto some embodiments.

FIGS. 44, 45, 46A-46C illustrate schematic diagrams of bet recognitiondevices with shoe devices according to some embodiments.

FIGS. 47 to 50 illustrate schematic diagrams of chip stacks according tosome embodiments.

FIGS. 51 and 52 illustrate schematic diagrams of bet recognition deviceswith camera layouts according to some embodiments.

FIGS. 53-56 are sample workflows, according to some embodiments.

DETAILED DESCRIPTION

Embodiments described herein relate to systems, methods and devices formonitoring game activities at gaming tables in casinos and other gamingestablishments. For example, embodiments described herein relate tosystems, methods and devices for monitoring card game activities atgaming tables. Each player, including the dealer and customer(s), may bedealt a card hand. Embodiments described herein may include devices andsystems particularly configured to monitor game activities that includebetting activities at gaming tables to determine bet data including anumber of chips in a betting area of the gaming table and a total valueof chips in the betting area.

The player bet data may be used by casino operators and third partiesfor data analytics, security, customer promotions, casino management,and so on. Games are not necessarily limited to card games, and mayinclude dice games, event betting, other table games, among others.

In accordance with an aspect of embodiments described herein, monitoringdevices may be used to retrofit gaming tables. The monitoring devicesmay be integrated with the gaming tables to provide a smooth workingarea in a manner that does not catch on cards or chips. The monitoringdevice may not require changing of a gaming table top as it may beintegrate within existing table top structure. An example of amonitoring device is a bet recognition device, as described herein.

Tracking bet activities that are on-going at a gaming facility is anon-trivial task that has myriad financial consequences. Accurate bettracking is important as it may be used to more closely monitor therevenues and outflows of the gaming facility, identify patterns (e.g.,theft, collusion), and provide an enhanced gaming experience. Forexample, tracked bet information, in the form of betting records, may beused to determine compensation levels for loyal players (e.g., theaccurate provisioning of “comps” in relation to overall casino returns),rebates, etc., or track dealer and/or game performance.

Bets are often performed in conjunction with games (e.g., baccarat,poker, craps, roulette) or events (e.g., horse racing, professionalsports, political outcomes), and traditionally, some bets are placedwith the aid of specially configured markers (e.g., chips). These betmarkers may have various markings on them, and are often distinguishedfrom one another so that it is easy to track the value of each of themarkers (e.g., denominations, characteristics). Some of the markers aredesigned with a particular facility in mind, and accordingly, may varyfrom facility to facility. For example, facilities may include casinos,gaming halls, among others.

Betting markers, such as chips, may have varying designs and markingsthat not only distinguish between chip types (e.g., chip values), butalso different series of chips having the same values (e.g., to reducethe risk counterfeiting and/or to enable tracking). For example, suchvariations may be purposefully and periodically introduced such thatcounterfeiters may have a harder time successfully copying chip designs.

Accordingly, a flexible implementation may be preferable so that adiverse range of conditions and chips can be used with the system. Forexample, in some embodiments, a system is provided that is configuredfor interoperation with a diverse range of chip types, and also toflexibly adapt in view of modifications to chip designs and markings. Insuch embodiments, the system is not “hard coded” to associate specificdesigns and markings with chip values, but rather, appliesmachine-learning to dynamically associate and create linkages as newchip types are introduced into the system. Interoperability may befurther beneficial where a single system can be provisioned to differentgaming facilities having different needs and environments, and thesystem may, in some embodiments, adapt flexibly in response to suchdifferences (e.g., by modifying characteristics of a referenceillumination on the chips, adapting defined feature recognitionlinkages, adapting imaging characteristics, image data processing steps,etc.).

The bet markers, such as chips, are often provided in physical form andplaced individually or in “stacks” that are provided in specific bettingareas on tables so that a dealer can see that a player has made a bet ona particular outcome and/or during a betting round. A game or event mayinclude multiple betting rounds, where a player is able to make aparticular bet in conjunction with a phase and/or a round in the game orevent. The betting may result in a win, loss, push, or other outcome,and the player may be paid chips equivalent to an amount of winnings.

The ability to track bets in real or near-real time may be of commercialand financial importance to a gaming facility. Inaccurate tracking ofbets may lead to increased management overhead and/or an inability toaccurate track betting, which may, for example, lead to missedopportunities to enhance player experience, or missed malicious behaviortrends. For example, analyzing betting patterns may indicate that someplayers are “gaming the system” by placing suspicious bets (e.g., due tocard counting, hole carding), or may indicate particularly profitablebets for the gaming facility (e.g., Blackjack insurance bets). The bettracking information may be utilized in conjunction with other types ofbackend systems, such as a hand counting system, a security managementsystem, a player compensation system (e.g., for calculating whencomplimentary items/bonuses are provided), etc. Bet recognition may alsobe used in gaming training systems, where players can be informed thattheir betting was not efficient or suboptimal based on computer-basedsimulation and calculation of odds (e.g., for Texas Hold-em poker,efficient betting may be determined based on mathematical odds and tablepositioning, especially for structured betting games and/or pot-limitand limit games, and may also be influenced by the presence of rulemodifications).

In some embodiments, bet tracking information is collected usingmachine-vision capable sensors that may be present on a gaming table orsurface, or other type of gaming machine. These machine-vision capablesensors monitor betting areas to determine the types of chips placed inthem, and estimate the value of bets, tracking betting as bettingprogresses from round to round and from game to game. As many gamingfacilities have invested significantly into their existing chips,tables, technologies and/or layouts, some embodiments described hereinare designed for flexibility and interoperation with a variety ofexisting technologies and architectures. Machine vision is not limitedto imaging in the visual spectrum, but may also include, in variousembodiments, imaging in other frequency spectra, RADAR, SONAR, etc.Machine vision may include image processing techniques, such asfiltering, registration, stitching, thresholding, pixel counting,segmentation, edge detection, optical character recognition, amongothers.

Accordingly, a bet tracking system may benefit from being able to beretrofit into existing tables and/or layouts, and interface with othertable and/or gaming facility management systems (e.g., to communicateinformation regarding betting activities). Machine-learning techniques(e.g., random forests) may be utilized and refined such that visualfeatures representative of different chip values are readily identified,despite variations between different facilities, lighting conditions andchip types. For example, such a system may not necessarily need to havehard-coded reference libraries of what chips should look like for eachvalue, and instead, may be flexibly provisioned during the calibrationprocess to build a reference library using real-world images of chips totrain a base set of features. Accordingly, in some embodiments, thesystem may be utilized without a priori knowledge of the markers presenton the various betting markers, such as chips. This may be useful wherea system may need to account for introduced variations in chip design,which, for security reasons, are not distributed ahead of introduction.

A potential challenge with tracking bets is that there are a diversityof betting markers, objects on a gaming surface, lighting conditionsthat may lead to complexities in relation to accurately determining whatbet markers are present, and further, what value should be attributed toa bet. Bets may be placed off-center by players, chips may not beuniformly stacked, chips may be obscuring one another, players mayobscure bets using their hands, players may be deliberately modifyingtheir bets (e.g., surreptitiously adding to a bet after cards have beendealt to obtain a higher payout), etc. Bet recognition also ispreferably conducted with minimal disruption to the operations of thegaming facility or player experience.

There may also be limitations on the amount of available computingresources, and given that many gaming tables operate with a high volumeof games per hour, there is limited time available for processing(especially where bet data is being tracked in real or near-real time).Gaming facilities may have computational resources available atdifferent locations, and these locations may need to communicate withone another over limited bandwidth connections. For example, there maybe some computing components provided at or near a gaming table suchthat pre-processing may be conducted on sensory data, so that acompressed and/or extracted set of data may be passed to a backend formore computationally intensive analysis. In some embodiments, thebackend may revert computed information back to the computing componentsprovided at or near a gaming table so that a dealer or a pit-boss, orother gaming employee may use an interface to monitor betting activities(e.g., to determine “comp” amounts, track suspicious betting patterns,identify miscalculated payouts).

Bet recognitions systems may utilize sensors positioned at a variety ofdifferent locations to obtain information. For example, systems mayutilize overhead cameras, such as existing security cameras. A challengewith overhead camera systems is that the presence of shadows, skewedimage angles, obstructions, have rendered some embodiments particularlycomplicated from a computational perspective, as issues relating to dataquality and the amount of visible information may lead to unacceptablylow accuracy and/or confidence in computationally estimated bet counts.

FIG. 1A illustrates a block diagram of a system for monitoring bettingactivities at gaming tables according to some embodiments. The systemmay be configured such that sensors and/or imaging components areutilized to track betting activities, generating sensory data that issent to a backend for processing. The betting activities may be providedin the form of chips being placed in betting areas, and the sensorsand/or imaging components may include machine-vision sensors adapted forcapturing images of the betting areas.

As depicted, the system includes bet recognition devices 30 (1 to N)integrated with gaming tables (1 to N). The bet recognition devices 30may include various sensors and imaging components, among other physicalhardware devices.

Each bet recognition device 30 has an imaging component for capturingimage data for the gaming table surface. The gaming table surface hasdefined betting areas, and the imaging component captures image data forthe betting areas. A transceiver transmits the captured image data overa network and receives calibration data for calibrating the betrecognition device 30 for the betting areas. Bet recognition device 30may also include a sensor component and a scale component, in someembodiments. The image data may, for example, focus on a particularregion of interest or regions of interest that are within the field ofview of the sensor component.

In some embodiments, the bet recognition devices 30 are hardwareelectronic circuitry that are coupled directly in or indirectly to agaming surface. In some embodiments, the bet recognition device 30 isintegrated into the gaming surface. The bet recognition device 30 may beprovided as a retrofit for existing gaming surfaces (e.g., screwed in,provided as part of a chip tray).

The bet recognition devices 30 may further include illuminatingcomponents or other peripheral components utilized to increase theaccuracy of the bet recognition. For example, an illuminating bar may beprovided that provides direct illumination to chip stacks such that theimaging component is more able to obtain consistent imagery, which mayaid in processing and/or pre-processing of image data. Anotherperipheral component may include the use of pressure sensitive sensorsat the betting area to denote when there are chips present in thebetting area, and in some embodiments, the weight of the chips (e.g.,which can be used to infer how many chips, which can be cross-checkedagainst the image data).

The bet recognition device 30 may have one or more processors andcomputational capabilities directly built into the bet recognitiondevice 30. In some embodiments, these computational capabilities may belimited in nature, but may provide for image pre-processing featuresthat may be used to improve the efficiency (e.g., file-size, relevancy,redundancy, load balancing) of images ultimately provided to a backendfor downstream processing. The bet recognition device 30 may alsoinclude some storage features for maintaining past data and records.Some implementations provide for a very limited window of processingtime (e.g., fast betting rounds or game resolution), and thepre-processing aids in speeding up computation so that it may beconducted in a feasible manner in view of resource constraints.

In some embodiments, the bet recognition device 30 contains multiplephysical processors, each of the physical processors associated with acorresponding sensor and adapted to track a particular bet area. In suchan embodiment, the system has increased redundancy as the failure of aprocessor may not result in a failure of the entirety of bet recognitioncapabilities, and the system may also provide for load balancing acrosseach of the physical processors, improving the efficiency ofcomputations. Each sensor may be tracked, for example, using anindividual processing thread.

The system includes a game monitoring server 20 with a processor coupledto a data store 70. In some embodiments, the game monitoring server 20resides on, near or proximate the gaming surface or gaming table. Forexample, the game monitoring server 20 may include a computing systemthat is provided as part of a dealer terminal, a computer that isphysically present at a gaming station, etc.

The game monitoring server 20 processes the image data received from thebet recognition devices 30 over the network to detect, for each bettingarea, a number of chips and a final bet value for the chips. The gamemonitoring server 20 may also process other data including sensor dataand scale data, as described herein.

The game monitoring server 20 is configured to aggregate game activitydata received from bet recognition devices 30 and transmit commands anddata to bet recognition devices 30 and other connected devices. The gamemonitoring server 20 processes and transforms the game activity datafrom various bet recognition devices 30 to compute bet data and toconduct other statistical analysis.

The game monitoring server 20 may connect to the bet recognition devices30 via bet recognition utility 40. The bet recognition utility 40aggregates image data received from multiple bet recognition devices 30for provision to the game monitoring server 20 in a tiered manner. Insome example embodiments, game monitoring server 20 may connect tomultiple bet recognition utilities 40.

Each bet recognition device 30 may be linked to a particular gamingtable and monitor game activities at the gaming table. A gaming tablemay be retrofit to integrate with bet recognition device 30. Betrecognition device 30 includes an imaging component as described herein.In some embodiments, bet recognition device 30 may also include sensorsor scales to detect chips.

Bet recognition utility device 40 connects bet recognition devices 30 tothe game monitoring server device 20. Bet recognition utility 40 may actas a hub and aggregate, pre-process, normalize or otherwise transformgame activity data, including image data of the gaming tables. In someembodiments, bet recognition utility 40 may relay data. Bet recognitionutility 40 may be linked to a group of gaming tables, or a location, forexample.

Bet recognition utility device 40, for example, may be a backend servercluster or data center that has a larger set of available computingresources relative to the game monitoring server 20. The bet recognitionutility device 40 may be configured to provide image data in the form ofextracted and/or compressed information, and may also receiveaccompanying metadata tracked by the bet recognition device 30, such astimestamps, clock synchronization information, dealer ID, player ID,image characteristics (e.g., aperture, shutter speed, white balance),tracked lighting conditions, reference illumination settings, amongothers.

This accompanying metadata, for example, may be used to providecharacteristics that are utilized in a feedback loop when bet outcomesare tracked. For example, the type of image characteristics or referenceillumination characteristics of the bet recognition utility device 40may be dynamically modified responsive to the confidence and/or accuracyof image processing performed by the bet recognition utility device 40.In some embodiments, the bet recognition utility device 40 extracts fromthe image data a three-dimensional representation of the betting andmaybe used to track not only betting values but also chip positioning,orientation, among others. This information may, for example, be used totrack patterns of betting and relate the patterns to hand outcomes, theprovisioning of complimentary items, player profile characteristics,etc.

The system may also include a front end interface 60 to transmitcalculated bet data, and receive game event requests from differentinterfaces. As shown in FIG. 2 , front end interface 60 may reside ondifferent types of devices. Front end interface 80 may provide differentreporting services and graphical renderings of bet data for clientdevices. Graphical renderings of bet data may be used, for example, byvarious parties and/or stakeholders in analyzing betting trends. Gamingfacilities may track the aggregate amounts of bets by account,demographic, dealer, game type, bet type, etc. Dealers may utilizebetting information on a suitable interface to verify and/or validatebetting that is occurring at a table, pit bosses may use the bettinginformation to more accurately determine when complementary items shouldbe dispensed and provided, etc.

Front end interface 60 may provide an interface to game monitoringserver 20 for end user devices and third-party systems 50. Front endinterface 60 may generate, assemble and transmit interface screens asweb-based configuration for cross-platform access. An exampleimplementation may utilize Socket.io for fast data access and real-timedata updates.

Front end interface 60 may assemble and generate a computing interface(e.g., a web-based interface). A user can use the computing interface tosubscribe for real time game event data feeds for particular gamingtables, via front end interface 60. The interface may include a firstwebpage as a main dashboard where a user can see all the live gamingtables and bet data in real time, or near real time. For example, themain dashboard page may display bet data, hand count data, player countdata, dealer information, surveillance video image, and so on. Bet datamay include, for example, total average and hourly average bets perhand, player or dealer, per hour bet data for each gaming table in realtime, and so on. The display may be updated in real-time.

The interface may include a management page where management users canperform management related functions. For example, the interface mayenable management users to assign dealers to inactive gaming tables orclose live gaming tables. An on and off state of a gaming table may senda notification to all instances of the interface. If a user is on themonitor management page when a new gaming table is opened, the user maysee the live gaming table updated on their display screen in real-time.The management page may also shows surveillance images of each gamingtable, and other collected data. The surveillance images may be used ortriggered upon detection of particular patterns of bet data at a gamingtable, for example.

Front end interface 60 may include a historical data webpage, which maydisplay historical bet data of a selected gaming table. It may allow theuser to browse the historical bet data by providing a date rangeselecting control. The bet data may be organized hourly, daily, monthly,and so on depending on the range the user chooses. The bet data alongwith the hand data and a theoretical earning coefficient may be used toestimate the net earnings of the gaming table over the selected dateperiod.

A server and client model may be structured based on receiving andmanipulating various sorts of game event data, such as hand count data,betting data, player data, dealer data, and so on. The interface may beexpanded to process other types of game data such as average bets perhands on a table. Bet data can be displayed on the monitor or managementpage in an additional graph, for example. The date range selection toolmay be used for analyzing the added data along with the bet data.Similarly, the main dashboard may show real-time statistics of both thebet data and the additional game data.

In some embodiments, the bet recognition utility device 40 may receiveactivation/deactivation signals obtained from various external devices,such as an external shoe, a hand counting system, a player accountregistration system, a pit boss/employee manual triggering system, etc.These external devices may be adapted to transmit signals representativeof when a betting event has occurred or has terminated. For example, aspecially configured dealer shoe may be operated to transmit signalswhen the dealer shoe is shaken, repositioned, activated, etc., or a handcounting system may be interoperating with the bet recognition utilitydevice 40 to indicate that a new round of betting has occurred, etc. Insome embodiments, betting may be triggered based on the particular gamebeing played in view of pre-defined logical rules establishing whenbetting rounds occur, when optional betting is possible (e.g.,side-bets, insurance bets, progressive bets), etc.

The system 10 may also integrate with one or more third party systems 50for data exchange. For example, a third party system 50 may collectdealer monitoring data which may be integrated with the bet datagenerated by game monitoring server device 20. As another example, athird party system 50 may collect player monitoring data which may beintegrated with the bet data generated by game monitoring server device20.

FIG. 1B is an example block schematic 100B illustrative of somecomponents of a bet recognition system 200, according to someembodiments. The components shown are for example only and may reside indifferent platforms and/or devices. The system 200 may include, forexample, an imaging component 202 including one or more sensors todetect and/or obtain image data representative of betting areas. Theimaging components 202 may be, for example, cameras, sensors, and maycollect image data in the form of video, pictures, histogram data, invarious formats. The image data may have particular characteristicstracked in the form of associated metadata, such as shutter speeds,camera positions, imaging spectra, reference illuminationcharacteristics, etc. In some embodiments, the imaging components mayprovide an initial pre-processing to perform preliminary featurerecognition, optical character recognition, etc. For example, the gamingsurface may have visual indicators which may be tracked as referencemarkers by the imaging components (e.g., optical position markersindicative of betting areas where bets may be placed).

An image processing engine 204 may be provided that is configured toreceive the images and to extract features from the images. In someembodiments, the image processing engine 204 segments and/orpre-processes the raw image data to remove noise, artifacts, and/orbackground/foreground imagery. For example, the image processing engine204 may be configured to visually identify the pixels and/or regions ofinterest (e.g., by using a combination of depth data and similarity/sizeinformation) regarding the chips. Specific stacks of chips may beidentified, along with their constituent chips. The chips may have“bounding boxes” drawn over them, indicative of the pixels to be usedfor analysis. Similarly, in some embodiments, “bounding boxes” are drawnover entire stacks of chips. The image processing engine 204 may extractfeatures from the bounding boxes and, for example, create a compressedtransform representative of a subset of the image information. Forexample, in some embodiments, various vertical, horizontal, or diagonallines of information may be drawn through a determined stack of chips,and samples may be obtained through tracking the image pixels proximateto and/or around a determined centroid for each of the chips.

In some embodiments, to account for variations in markings (e.g.,vertical stripes), the pixels (e.g., horizontal pixels) estimated tocomprise a particular chip are blurred and/or have other effectsperformed on them prior to extraction such that the centroid and itssurrounding pixels are representative of the chip as a whole.

The image processing engine 204 may also extract out a particular heightof the chips, and this information may be utilized to determine thegeneral size and/or makeup of the stack of chips. For example, knowledgeof the chip stack, distance, and height of specific chips may permit forthe segmentation of pixel information on a per-chip basis.

The image recognizer engine 206 may obtain the extracted and compressedinformation from the image processing engine 204, applying recognitiontechniques to determine the actual chip value for each chip in therelevant region of interest. As the image recognizer engine 206 receivesa set of features, the image recognizer engine 206 may be configured toutilize a classifier to determine how well the feature set correspondsto various reference templates. In some embodiments, the classifierprovides both an estimated value and a confidence score (e.g., a marginof error indicative of the level of distinction between potential chipvalue candidates). Where the chip value cannot be reliably ascertainedthrough the reference templates, a notification may be provided toeither request re-imaging with varied characteristics, or to generate anerror value. For example, features may be poorly captured due to changesin ambient lighting and/or environmental shadows, and the notificationfrom the classifier may control a reference lighting source to activateand/or modify illumination to potentially obtain a more useful set ofimage features.

In some embodiments, the image recognizer engine 206 may dynamicallyprovision computing resources to be used for recognition. For example,if the image recognizer engine 206 identifies that a larger amount ofprocessing will be required in view of a large volume of poor qualityimage data, it may pre-emptively request additional processing resourcesin view of a requirement to complete processing within a particulartimeframe. Conversely, in some embodiments, where image data is ofsufficiently high quality to quickly and accurately conclude that a chipis a particular type of chip, processing resources may be freed up.

A rules engine subsystem 208 may be provided in relation toclassification of chip image data/features to chip values. The rulesengine subsystem 208 may, for example, include tracked linkages andassociations that are used by the classifier to determine a relationshipbetween a particular reference feature set. In some embodiments, therules engine subsystem 208 includes weighted rules whose weightsdynamically vary in view of updated reference feature sets or accuracyfeedback information (e.g., indicated false positives, false negatives,true positives, true negatives), among others. The rules enginesubsystem 208 may also include logical processing rules that controloperation of various characteristics of the classifier, the referenceillumination, processing characteristics, etc.

A game monitoring engine 210 may obtain the tracked chip/bet values foreach bet, for example, from a plurality of imaging components 202,processing engines 204 and/or recognizer engines 206, and maintain aninventory of betting data, which may be stored in data storage 250. Thegame monitoring engine 210 may be adapted to provide real ornear-real-time feedback, and also to perform various analyses (e.g.,overnight processing). The game monitoring engine 210 may identifypatterns from combining bet tracking data with other data, such asplayer profile information, demographics, hand counting information,dealer tracking information, etc.

An administrative interface subsystem 212 may be provided foradministrative users to control how the system operates and/or torequest particular analyses and/or reports. A user interface subsystem214 may provide, for example, various graphical interfaces forunderstanding and/or parsing the tracked bet recognition data. Thegraphical interfaces may, for example, be configured to generatenotifications based on tracked discrepancies, etc. The variouscomponents may interoperate through a network 270.

In some example embodiments, game monitoring server 20 may connectdirectly to bet recognition devices 30. FIG. 2 illustrates a blockdiagram 200 of another system for monitoring game activities at gamingtables according to some embodiments. System may include bet recognitiondevice 30 at gaming table with defined bet areas 34 on the gaming tablesurface. In this example, bet recognition device 30 directly connects togame monitoring server 20 to provide image data for the gaming tablesurface and the bet areas 34.

FIG. 3 illustrates a block diagram 300 of a further system formonitoring game activities at gaming tables according to someembodiments involving betting data and hand count data. Card gameactivities may generally include dealing card hands, betting, playingcard hands, and so on. Each player, including the dealer and customers,may be dealt a card hand. For a card game, each active player may beassociated with a card hand. The card hand may be dynamic and changeover rounds of the card game through various plays. A complete card gamemay result in a final card hand for remaining active players, finalbets, determination of winning card hands amongst those active players'hands, and determination of a winning prize based on winning card handsand the final bets. At different rounds or stages of the game differentplayers make bets by placing chips in bet regions on the gaming tablesurface.

Bet recognition device 30 and hand count device 32 may be integrated ateach gaming table for capturing image data for bets and counting thenumber of card hands played at the particular gaming table. Hand countdevice 32 is another example of a game monitoring device. A player mayhave multiple card hands over multiple games, with different betsassociated with hands. Hand count device 32 may count the number ofhands played at a gaming table, where the hands may be played by variousplayers. Bet recognition device 30 may collect image data for server 20to calculate bet data for different hands and players.

Hand count device 32 may determine a player hand count may be over atime period. Bet recognition device 30 may determine bet data over atime period, using timestamps, for example. Server 20 may correlate handcount and bet data using timestamps or time periods, for example. Theinformation may be stored on data store 70, and presented on front enterinterface 60.

Bet recognition device 30 may associate bet data with a particulargaming table, dealer, customers, geographic location, subset of gamingtables, game type, and so on. Similarly, hand count device 32 mayassociate hand count data with a particular gaming table, dealer,customers, geographic location, subset of gaming tables, game type, andso on. For example, bet data may be associated with a timestamp andgaming table identifier to link data structures for further dataanalysis, processing and transformation.

Metadata is collected alongside image data and may be associated (e.g.,using pointers, labels, metadata tags) with the image data to indicateadditional information, such as checksums (e.g., for redundancy andimmutability), timestamps, player information, hand count information,bet round information, lighting conditions, reference lightingcharacteristics, confidence score associated with image data, sensors inuse, processor in use, etc.

Image data, along with other metadata may be encapsulated in the form ofinformation channels that may be use for transmission and/or otherwiseencoded. In some embodiments, 10 or more channels of information areprovided by the bet recognition device 30, and the channels may include,for example, image data taken with different color balances andparameters, image data from different sensors, metadata, etc.

Each bet recognition device 30 may transmit image data or other bet datato bet recognition utility 42 for provision to game monitoring server20. Each hand count device 32 may transmit hand count data from a sensorarray to hand count utility 42 for provision to game monitoring server20. Further details on hand count device 32 and game monitoring server20 for calculating hand count data is described in U.S. ProvisionalApplication No. 62/064,675 filed Oct. 16, 2014 the entire contents ofwhich is hereby incorporated by reference.

Hand count device 32 may include sensors, such as for example lasersensors with optical emitters and receivers. Laser sensors, instead ofother types such as ambient light sensors, may be advantageous to reducethe effect of lighting in the environment, to not require special tabletop felt material, to waterproof the device, and so on. Ambient lightsensors may not work well if a part of the table is not well lit, asthose types of sensors are looking for darkness for object detection.Hand count device 32 may use optical receiver and emitter sensors thatlook for light for object detection. Additional types of sensors includeradio frequency and optics. The sensors may be organized to form asensor array. Hand count device 32 may further include an infraredreceiver and infrared emitter or transmitter for electronic dataexchange. The sensors are particularly configured and positionedrelative to the play area and bet area on the gaming table. For example,a sensor array may be positioned proximate to the card play area and betarea. The device may be configured to provide a particular distancebetween sensor and card play area or bet area, such as a one centimeterdistance, for example.

Bet recognition device 30 may similarly retrieve image data captured byimaging component. Hand count device 32 may receive power and retrievedata off of sensors used for monitoring game activities. Both hand countdevice 32 and bet recognition device 30 generate game activity data(which may also be referred to herein as game event data) for provisionto game monitoring server 20. Game activity data may include hand countdata events, such as hand start event data and hand stop event data.Hand start event data indicates the start of a new hand. Hand stop eventdata indicates the end of a hand. Together with timestamps these valuesmay be used to compute hand duration and other data values. Bet data mayalso be linked by timestamps. The sensors of hand count device 32 may bepositioned on the gaming table to detect card hand activities andtrigger hand start events and hand stop events. The sensors may deliverreal-time data regarding card play activity, including hand start eventdata and hand stop event data. The imaging components may also deliverreal-time image data regarding bet activities. The imaging component ofbet recognition device may be mounted or integrated into gaming table tocapture real-time image data for bet areas on the gaming table surface.

In some embodiments, the clocks of the bet recognition device 30, thehand count device 32, game monitoring server 20 are synchronizedtogether to ensure that data is readily interpretable regardless ofsource.

Bet recognition device 30 may be configured with particular triggerevents, such as detection of chips or objects in defined bet areas onthe gaming table by sensors. The trigger events may trigger imagingcomponent to capture image data for calculating bet values for thechips. A timing or threshold value may be set off to triggertransmission of game event data used to calculate bet data and countcard hands. An example trigger may be sensor activation for a thresholdvalue, for example two, three or four seconds. Another example triggermay be sensor deactivation for a threshold value.

Game activity data may include bet data, player count data and handcount data, which may be valuable for casinos for security, management,and data analytics. For example, a casino may determine a link between agame and a dealer, and also a dealer and a customer, through the betdata, the hand count data and the player count data. A casino mayprovide real-time compensation to players using the bet data, handcount, and player count data. Accordingly, the systems, devices andmethods in accordance with embodiments described herein may providevarious levels of granularity and specificity for game activity data,using the bet data, hand count data, player count data, and othergenerated game activity data values. There may further be third partyplayer tracking and/or dealer tracking data 50 that may be utilized inrelation to performing analysis and reporting.

A gaming table includes one or more bet areas. FIGS. 4A-4C illustrates aschematic diagram of bet areas 34 monitored by a bet recognition device30 according to some embodiments.

As illustrated in FIGS. 4A-4C, a challenge with tracking betting andchips is the ability to obtain sufficient quality and resolution toaccurately track bets. FIG. 4A is an overhead or elevational top view400A, according to some embodiments. FIG. 4B is a perspective view 400B,according to some embodiments. FIG. 4C is an overhead or elevational topview 400C in relation to a camera system 30, according to someembodiments. Bets 402 may be placed in a betting area 34 on a gamingtable, and for example, betting areas may be demarcated through the useof machine-vision interpretable boundaries, etc. The bets may includevarious chips, and the chips may have different values attributed to thechips. The chips may be placed in one or more stacks within the field ofview of the camera system 30.

These boundaries, for example, may appear to be a single visual ring toa player, but in some embodiments, layers of different boundaries (e.g.,as different rings) may be utilized to more granularly indicate slightdifferences in positioning of chips. For example, boundaries that areonly visible in the infrared or ultraviolet lighting may be used, andthese may be tracked by machine-vision sensors to demarcate where thebetting area begins, ends, different parts of a betting area, etc. Forexample, such granular boundaries may be helpful where small differencesin depth, positioning, etc. may impact the accuracy of such a system.Visual and/or other types of optical markers may be used to serve asreference areas for depth calculations

While some other systems have utilized overhead cameras positioned overa table or based on tracking images captured from overhead securitycameras, these systems have had difficulties obtaining sufficiently highquality images of chips placed in betting areas to be able to accuratelyand responsively track bet counting. For example, using an overheadcamera may lead to an inconsistent number of pixels being used to trackeach chip, the number of available pixels being limited due to theobstruction caused by chips being placed on one another (e.g., anoverhead camera directly above a stack of chips may not be able toadequately identify chips underneath the top chip of a stack, or if itis placed at an overhead some distance away, the system may not have agood view of the individual chips within the stack as there may eitherbe obstructions or the specific angle of the chips may cause undesirableshadowing effects. For example, depending on a camera's ability toobtain images, chips deep in a stack of chips may all appear to be blackas the chips in the stack end up casting shadows on one another.Perspective views of chips may computationally difficult to analyze inview of the required transformations to obtain a representative set ofpixels.

Similarly, it may be difficult to account for variations of ambient andenvironmental lighting that may be illuminating the chips themselves.Where differences in illumination intensities are utilized to track chipvalues and distances, such variations may reduce the accuracy ofreadings or provide false positive/false negative readings.

In some embodiments, imaging components (e.g., cameras) are placed andpositioned to have a substantially horizontal sensor angle when viewingthe chips, a depiction of which is provided at FIG. 4B. Substantiallyhorizontal may mean substantially parallel to a plane of the gamingsurface.

The imaging components may be adapted such that the imaging component isdirected towards the betting areas from or near the perspective of adealer. Such a configuration may be helpful in ensuring that the chipsare less obstructed, and provide a sufficient view of the sidewalls ofthe chips. An “offset angle” may be provided where the imagingcomponents, while “looking” substantially parallel at the sidewalls ofthe chips, due to the stacked nature of chips, may aid in obtaining asmany pixels as possible.

As described, the imaging component angle may be important to ensurethat as many pixels of information can be extracted from amachine-vision image that are representative of chips. The imagingcomponent itself may also require to be off-set from the gaming surface(e.g., at a particular altitude or height) such that the sensing is notblocked by the presence of objects on the gaming surface, such asplaying cards, dice, markers, etc. For example, a card may be curled ata corner, and a sensor placed directly horizontal and in contact withthe gaming surface may end up being obstructed by the cards (and thusunable to read the value of the chips). The horizontal angle, forexample, may be an angle between −5 to 5 degrees, and the altitude maybe between 0.2 cm to 1.0 cm. While the image obtained may be direct forsome chips, there is nonetheless some angle for chips that are at thetop or the bottom of the stack.

In some embodiments, the imaging component may be utilized incombination with an illumination strip, the illumination strip (e.g.,lights, infrared lights, ultraviolet lights) providing a “referenceillumination” against the sidewall of the chips.

For example, the illumination strip may be placed above or below theimaging component and may provide illumination in all or a portion ofthe field of view of the imaging component. The illumination providedmay be static (e.g., a regular light) or controlled (e.g., acontrollable light). The illumination characteristics may be modified(e.g., filters applied, the amount of total light controlled, thespectral makeup of the light may change, etc.). The illuminationcharacteristics may be used in various ways, for example, to ensure thatat a minimum number of pixels are able to be captured per chip, toensure that there is constant reference illumination despite changes inambient lighting, etc.

In some embodiments, illumination characteristics are modified inresponse to requests from the system. For example, the system maydetermine that there indeed are chips providing in a particular area,but the system is experiencing difficulty in assessing the value of thechips (e.g., due to environmental, ambient illumination, distortions.

In some embodiments, the imaging component and/or the illumination isprovided on a physical track or pivot and is able to modify viewingangles and/or positions (or both) in response to poor image recognition.For example, at some angles, chips may be covered by shadows (especiallythe bottom chips of a stack) and due to the shadowing, may appear to beindistinguishable from other chips or erroneously recognized. Theclassifier may identify a low level of confidence in the recognition andin response, generate a control signal to move the camera and/or pivotthe camera and/or other sensors, such as depth sensors, etc.

A control system may note that the recognition device is havingdifficulty (e.g., by an increase in error rate, failing to meeting apre-defined threshold of pixels required to make an accuratedetermination) and issue a command control to the illumination device tocontrol the illumination device to more actively “light up” the chips sothat a better picture may be taken for conducting image recognition.

Similarly, bet recognition devices may be designed to operate inenvironments where the amount of environmental and ambient lightingvaries quite frequently. Light may be provided from natural sources(e.g., windows), or from artificial sources. Ambient lighting may occurfrom artificial sources that are incident to the bet recognition device,such as the lights provided on other machines, room lighting, etc. Insome embodiments, a gaming facility may purposefully modify the lightingconditions to impress upon the players a particular ambience or theme.Individuals at the facility may be smoking, casting shadows, etc.

These variations may significantly impact the ability of the system toperform bet recognition. A commercial consideration as to how the systemfunctions is the ability to operate the system in a variety of differentenvironments having different lighting conditions. For example, a betrecognition system may require some level of portability as the systemmay be moved around a gaming facility over its lifetime, or sold and/ormoved between different gaming facilities.

In some embodiments, the aspect ratio associated with the imagingcomponent may be a factor for consideration. For example, if the imagingcomponent was a 1080p camera, it means it has more pixels horizontallythan vertically, so the extra resolution in the width is more valuablein measuring the thickness of the chip. Rotating from a landscapeorientation to a portrait orientation would allow for more resolutionfor distinguishing chips from one another within a stack, potentiallyoffering more detail to for downstream processing.

In some embodiments, an illumination strip provides the referenceillumination, and the reference illumination may be provided in asubstantially horizontal view relative to the sidewalls of the chips.The reference illumination may, relative to overhead camera systems,provide more direct and relatively unobstructed illumination to the chipsidewalls, making any machine-vision interpretable markings more visibleand easy to distinguish. As an example in the context of machine vision,particular colors may be difficult to distinguish from one another(e.g., red from pink), and similarly, striped markings may also bedifficult to process as poor lighting may impact the ability todetermine how thick a line is, etc. This problem may be particularlyexacerbated if the machine-vision is not operating in the same rangewavelengths as human vision, for example, if the machine vision operatesin infrared, ultraviolet ranges, monochromatic ranges, etc.

The reference illumination may be provided in proximate to orsubstantially at the same location as the imaging components. Forexample, the reference illumination may be provided in the form of anillumination strip running across a sensor housing. In some embodiments,the reference illumination is provided in the form of spaced-apart lightsources.

Accordingly, in some embodiments, the reference illumination inaccordance with control signals such that the reference illuminationcharacteristics (intensity, spread, spectral makeup, etc.) may bemodified and monitored to dynamically adjust and/or control forvariations from light provided from other sources For example, controlsignals may be provided, which when processed by the illumination strip,the illumination strip changes an intensity of the referenceillumination based at least on ambient lighting conditions. The controlsignals may be adapted to implement a feedback loop wherein thereference illumination on the one or more chips is substantiallyconstant despite changes to the ambient lighting conditions.

In some embodiments, rather than, or in combination with changing thereference illumination to provide a constant lighting condition, thereference illumination is adapted to monitor a confidence levelassociated with the bet recognition processing from machine-visionimages that are provided to a backend system. For example, if thebackend image processing system indicates that there are significantaccuracy and/or confidence issues, the backend image processing systemmay be configured to generate a control signal requesting modificationsto the reference illumination relative to the chips themselves. Outcomesmay be monitored, for example, by using a feedback loop, and controlledsuch that an optimal amount of reference lighting is provided. In someembodiments, the reference illumination is not constant, but is ratheradjusted to ensure that a sufficiently high level of confidence isobtained during image processing. In some embodiments, referenceillumination may be provided in a strobe fashion and/or otherwiseintermittently used when image processing capabilities are impacted(e.g., a transient shadow passes by, the chips are momentarilyobstructed by the hand of a player or the dealer, etc.).

The reference illumination, to save energy, may, in some embodiments, becontrolled such that it can be turned on whenever additionalillumination is required.

Embodiments described herein provide a game monitoring server 20configured to calculate a red green blue (RGB) Depth Bounding Area for agaming table to calibrate the corresponding bet recognition device 30.

Game monitoring server 20 and bet recognition device 30 calibrates eachbet area to ensure that only the chips in the bet area are counted, andnot chips in other areas of the gaming table that are not in play. Thebet area may be defined in response to input received at front endinterface 60 providing a graphical display of the gaming table surfaceand using an input device (e.g. a keyboard and mouse) to define aregion. As another illustrative example, the bet area may also bedefined by positioning chips in the bet area and nothing else on thegaming table.

Game monitoring server 20 and bet recognition device 30 mayautomatically implement bet area calibration by scanning the gamingtable and detecting any chips on the gaming table surface. If the chipson the gaming table are directly inside the bet area then gamemonitoring server 20 will automatically record xyz coordinate values forthe detected chips.

The game monitoring server 20 may be configured for performing varioussteps of calibration, and the calibration may include developing anarray of reference templates in relation to the particular set up of agaming surface or gaming table. For example, the reference templates mayinclude what chips “look like” at a particular table in view of usualgameplay settings, etc. Further, the reference templates may tracklighting conditions across the span of a day, a season, in view ofupcoming events, nearby light sources (e.g., slot machines), etc. Newtemplates may be provided, for example, when new chips or variations ofchip types are being introduced into circulation at a particular gamingfacility. In some embodiments, such introduction of new chips mayrequire a machine-learning training phase to be conducted to build areference library.

The calibration may be conducted, for example, on an empty table todetermine where the betting areas should be, where any demarcationsexist, etc. The calibration may be used to “true up” color settings,lighting conditions, distances, etc. For example, the referencetemplates may be indicative of how many pixels are generally in a chipin a first betting area, based on their position on a stack of chips,etc. The calibration may also track the particular height of chip stacksbased on how many chips are in the stacks and what kind of chips are inthe stack. These reference values may be stored for future use duringbet recognition, and may be compressed such that only a subset offeatures are stored for reference. The subset of features stored, forexample, may be utilized in a pattern recognition and/or downstreammachine-learning approach where relationships are dynamically identifiedbetween particular features and recognized bets.

The calibration may be conducted with reference games and betting, andtracked against manual and/or semi-manual review to determine accuracyand features for extraction, and characteristics associated with the betrecognition may be modified over time. For example, theprocessing/pre-processing steps may be modified to change definitions ofbet areas, bounding boxes, what image features are considered backgroundor foreground, how lighting needs to be compensated for in view ofchanging lighting conditions, transient shadowing, etc.

Embodiments described herein provide a game monitoring server 20configured to monitor betting activities by calculating RGB and DepthData for chips detected within bet areas of the gaming table.

The game monitoring server 20 may be configured to generate anelectronic representation of the gaming table surface. The gamemonitoring server 20 is configured to process the captured chip dataimages by segmenting the background chips and other data from the gamingtable images relative to the distance from camera component of the betrecognition device 30, or relative to the position on the gaming table.The bet recognition device 30 may only capture and transmit portions ofthe image data relating to the chip stack itself to the game monitoringserver 20 via bet recognition utility 40. In accordance with someembodiments the game monitoring server 20 or bet recognition utility 40receive image data for the gaming table surface and perform processingoperations to reduce the image data to portions of the image datarelating to the chip stack itself. The game monitoring server 20implements image processing to transform the image data in order todetect the number of chips in the betting area and ultimately the finalvalue of the chips.

In some embodiments, the electronic representation of the gaming tablesurface is used as a streamlined approach to extracting informationrelevant to the bet recognition. For example, the gaming table surfacemay be represented in two-or three dimensional space and used forcoordinate positioning. There may be defined bet areas that are providedbased on position, etc. and in some embodiments, the actual bet areasmay include further markings that may or may not be visible to humanplayers that are used for machine vision boundary demarcation and/ordepth calculations. For example, there may be areas indicated toindicate depth (e.g., if a particular boundary is covered, the boundaryis known to be at position (x, y), and therefore a chip stack is atleast around or covering position (x, y).

The game monitoring server 20 may utilize the electronic representationin generating a streamlined set of compressed information that is usedfor downstream analysis, such as for bet recognition, confidence scoretracking, machine learning, etc. For example, the electronicrepresentation may be updated as chips are placed into betting areas andsensed by the sensors. The sensors may track various elements ofinformation associated with the chips and modify the electronicrepresentation to note that, with a particular confidence level, that astack of chips has been placed, the stack of chips having a particularmakeup and value of chips, etc. The game monitoring server 20 may thenextract out only specific features and discard the other information inpreparing a compressed set of information representative of the betsbeing placed on a gaming surface (e.g., only a simple set of depthcoordinates, the estimated make-up of the chips in the stack, confidencevalues associated with how accurately the system considers itsassessments to be, etc.).

Embodiments described herein provide a game monitoring server 20configured to monitor betting activities by calculating depth data forchips detected within bet areas of the gaming table. Depth data can becaptured with a variety of different processes using different imagingcomponents. For example, an imaging component may include stereo cameras(e.g., RGB cameras) mounted in different positions on the gaming tableto capture image data for the betting areas. An imaging component withstereo cameras may have two or more black/white or RGB cameras, forexample. As another example, an imaging component may include a depthaware camera using Infrared (IR) and Time-Of-Flight (TOF). An imagingcomponent with depth cameras may use IR TOF or IR projection ofstructured light or speckled pattern.

Depth data may be an important output in relation to machine-visionsystems. For example, the distance from a chip may indicate how manyavailable pixels would make up chips in an image of chips, etc. Thenumber of available pixels may determine how a bounding box may be drawn(e.g., dimensions) around a chip, a stack of chips, etc., and in someembodiments, may be a factor in automatic determinations of confidencescores associated with machine-vision estimations of the values of thechips (e.g., if there are only 12 pixels available due to the stackbeing far away for a particular chip, and the pixels are impacted bypoor lighting conditions and partial obstructions, the confidence scoremay be particularly low, especially if the chip has markers that aredifficult to discern in poor lighting conditions).

Depth data may be generated based on, for example, tracking parallaxeffects (e.g., by moving a single sensor), stereoscopic effects (e.g.,by comparing parallax in two different cameras), pressure sensors in thebetting areas, range finding radar (e.g., Doppler radar), UV lightdispersion/brightness levels, distortion effects, etc.

Where sensors may be obstructed, depth data may be estimated from anaggregated set of captured images over a duration of time. For example,differences between each of the plurality of captured images may becompared to estimate the presence of the one or more chips despite thepresence of the one or more obstructing objects that are partially orfully obstructing the one or more chips from being sensed by the one ormore sensors.

The depth data may be determined in combination with a confidence score.For example, if a chip is particularly far away, there may be a limitednumber of pixels to analyze regarding the chip. The number of pixelsavailable may be further reduced if lighting conditions are poor, thechips are obstructed, there are imaging artifacts, etc., andaccordingly, a lower confidence score may be presented.

FIGS. 5 to 7 illustrate example images taken from a bet recognitiondevice mounted on a gaming table according to some embodiments. Theimage data for the images may include depth data taken from atable-mounted depth camera. The example images illustrate the table andbet area rendered in three-dimensions (3D). The image data definesstacks of chips in the bet areas in 3D defined using X, Y, and Zcoordinate values. The game monitoring server 20 may represent a gamingtable surface as a model of X, Y and Z coordinate values includingbetting areas.

As depicted in FIG. 5 , the sensors may take images 500 of the gamingsurface, including bet area 502/502′, cards 508/508′, stacks of chips504/504′/506/506′, etc. As noted in FIG. 5 , there may be some chips inthe foreground 504/504′, some in the background 506/506′, there may beother background images, etc. The images may be taken in human visibleand/or non-human visible wavelength ranges. For example, the images mayutilize infrared, ultraviolet, and accordingly, some wavelengths may bemore prominent than others. Another image is depicted in FIG. 6 , havingbet area 602/602′, cards 608/608′, stacks of chips 604/604′/606/606′,etc. As noted in FIG. 6 , there may be some chips in the foreground604/604′, some in the background 606/606′.

FIG. 7 is a compressed representation 700, wherein non-chip imagery hasbeen filtered out, and the image primarily consists of images of stacksof chips 702, 704, and 706. Filtering techniques, for example, includethe use of edge detection algorithms (e.g., difference of Gaussians).The representation 700 may be compressed such that chips are detectedamong other features, so that various regions of interest can beidentified. In some embodiments, representation 700 is combined withdepth data so that background and foreground chips may be distinguishedfrom one another. For example, chips that may be within a betting areaindicative of a bet may also have chips that are in the background(e.g., chips that the player has in the player's stacks that are notbeing used for betting). Depth data may be used to distinguish betweenthose chips that are in the betting area as opposed to those chips thatare out of the betting area.

In some embodiments, the imaging component may include a wide anglecamera. The wide angle camera can used to capture image data for all betareas game monitoring server 20 may implement correction operations onthe image data to remove warping affects. FIG. 8 illustrates an exampleschematic of a bet recognition device 30 with a wide angle camera.

In some embodiments, the imaging component may include three cameras802, 804 and 806 per gaming table. Three cameras may be mounted on agaming table in a configuration in front of the dealer position on thegaming table. Each camera may be dedicated or responsible for two betareas. When a hand has started (e.g., as per a detected hand startevent) each camera may capture image data of their respective bet areasfor transmission to game monitoring server 20. The game monitoringserver 20 stores the captured image data in a central data store 70.When new image data is sent to the data store 70 it may be processed bygame monitoring server 20 using the recognition process describedherein. The processed image data may generate bet data including thenumber of chips in a bet area and the total value of chips in the betarea. Game monitoring server 20 stores calculated bet data at data store70. The bet data may be linked with timestamp which is also recorded atdata store 70. When all image data captured by the three cameras hasbeen processed, image data for the hand played is recorded to the datastore 70. Game monitoring server 20 is operable to correlate the capturehand event data to the bet data to calculate bet values for particularhands.

The cameras of imaging component can be installed into the back bumperlocated between the dealer position and the gaming table. Thisinstallation process simplifies retrofitting existing gaming tablesallowing the casino operator to replace the back bumper without havingto impact the gaming table felt or surface.

FIGS. 8 and 9 illustrate example images of a bet recognition device 30mounted on a gaming table according to some embodiments. Theillustrative example 900 shows three cameras as an imaging component ofthe bet recognition device 30. The cameras are installed into the backbumper of the gaming table. Game monitoring server 20 can stitchtogether image data captured by different cameras to recreate a 3D modelof the table surface using X, Y and Z coordinate values. In some exampleembodiments, the game monitoring server 20 may evaluate image dataindependently for each camera. Game monitoring server 20 uses thecaptured image data to generate a 3D model of the gaming table surface.Game monitoring server 20 processes the image data by isolating imagedata for all bet areas and cropping the image data for all bet areas forfurther processing. Game monitoring server 20 processes the image datafor the bet area in order to find bet value for each bet area and totalbet or chip amount for each bet area.

In some embodiments, the 3D model is utilized to generate arepresentation of where the system interprets and/or estimates the chipsto be. The 3D model may be adapted to extrapolate position informationbased on known sizes and/or dimensions of chips, as the image data mayoften only include an incomplete set of information required to generatea 3D model. Multiple images may be stitched together to derive the 3Dmodel.

FIGS. 10 and 11 illustrate example images of a bet recognition devices30 according to some embodiments. FIG. 10 is an illustration 1000 of anexample bet recognition device 30 including three cameras capturingdifferent portions of the gaming table surface. FIG. 11 is anillustration 1100 that illustrates another example bet recognitiondevice 30 including a camera.

FIG. 12 illustrates a schematic diagram of another example betrecognition device 30 according to some embodiments. The bet recognitiondevice 30 may include a moving camera according to some embodiments. Alinear bearing can be used alongside a stepper motor to move the camerafrom one side of the gaming table 1204 to the other. This allows for onecamera system to capture image data of the gaming table surface fromdifferent angles (as denoted by the dashed lines). The number of imageframes captured and the interval at which the image frames are capturedcan be defined. The imaging component may be an RGB Camera 30 or two RGBcameras 30′, or one depth camera 30″, for example.

The movement of the camera may be used, for example, to assess depthusing parallax measurements, to stitch together images in generating a3D model, etc.

Game monitoring server 20 implements a chip recognition process todetermine the number of chips for each betting area and the total valueof chips for the betting area. For example, when a new hand is detectedat a gaming table, bet recognition device 30 captures image data of eachbet area on the gaming table surface and sends the captured image datato game monitoring server 20.

In some embodiments, bet recognition device 30 isolates image data foreach bet area from image data for a larger portion of the gaming tablesurface and sends cropped image data only for the bet area to the gamemonitoring server 20. In other embodiments, game monitoring server 20isolates image data for each bet area from image data for a largerportion of the gaming table surface.

Game monitoring server 20 processes each image frame of the capturedimage data and segments image data defining chips for each chip by sizeand color to determine a total number of chips for the betting area.Game monitoring server 20 detects a face value for each chip. Datarecords of data store 70 may link color and size data to face value.Game monitoring server 20 may also implement number recognition on theimage data for chips to detect face value. Game monitoring server 20calculates the total value of the bet by multiplying the number of chipsdetected by the face value of the chips.

FIGS. 13A, 13B and 14 illustrate example images taken from a betrecognition device mounted on a gaming table and processed images aftertransformation by server according to some embodiments. The images1300A, 1300B, and 1400 illustrate segments generated by the gamemonitoring server 20 to determine the number of chips and the face valueof the chips. Game monitoring server 20 stores the bet data in datastore 70.

Chips 1300-1314 may be processed by size, and color (e.g., in thisexample, 1302 is a different type of chip than the others), and thesensors and/or imaging components may obtain data, including images invisual spectrum or non-visual spectrum. For example, some markings maybe only shown in infrared or ultraviolet, or may fluoresce in responseto the reference illumination applied to the chips. Texture and shape,in some embodiments, are also tracked based on visually apparentfeatures.

As depicted in FIG. 13B, the images may be processed such that thesidewalls of the chips can be analyzed, by identifying a region ofinterest represented, for example, by the pixels within a “bounding box”1316 and 1316′ around each of the chips. Depending on the amount ofavailable resources, the determination of the region of interest may beconducted at a backend server, or during pre-processing on a devicecoupled to the gaming table, in various embodiments.

Game monitoring server 20 watches for new captured image data for thebet area and processes the new captured images against existing datarecords for bet data stored in data store 70. Chips in the bet area arechecked for size, shape, and color to ensure accuracy. FIGS. 13 and 14illustrate that only the chips in the bet area are detected by the gamemonitoring server 20 bet recognition process. Once processing iscompleted, the game monitoring server 20 sends the bet values of eachbet area or region to the data store 70. In some example embodiments,game monitoring server 20 associates a hand identifier to the bet datavalues. A timestamp may also be recorded with the bet data values storedin data store 70.

Embodiments described herein provide a bet recognition device 30 withboth an imaging component and a sensor component. Bet recognition device30 captures image and sensor data for provision to gaming monitoringserver 20 to calculate bet data.

FIG. 15 illustrates a schematic diagram 1500 of a sensor array devicefor bet recognition device according to some embodiments. A betrecognition device may include a microcontroller, a sensor array networkand connection cables. The microcontroller may run the logic level codefor checking onboard sensors (e.g. sensors integrated into the gamingtables via client hardware devices 30) for pre-defined thresholdstriggering capture of image data to determine bet data. Themicrocontroller may also emulate a serial communication protocol for thehost. The sensor array network may include interconnected sensors thatcan communicate with each other. The sensors may be integrated with agaming table and positioned relative to playing area of the table. Theymay be all connected via the microcontroller and routed accordingly. Aconnection cable may process the digital serial signal and allow thedevice to connect via USB or other protocol (e.g. wireless) to acomputer with a free port. The data may be transmitted via the USB cableor other protocol and may be read by a small utility on the hostcomputer.

FIG. 16 illustrates a schematic graph 1600 of the amplitude of thereceived signal from a sensor and/or imaging component over timeaccording to some embodiments.

The following is an illustrative example measurement setup for a scenepoint. A sensor estimates the radial distance by ToF or RADAR principle.The distance, ρ, is calculated at time T with electromagnetic radiationat light speed c, is ρ=cτ. The transmitter emits radiation, whichtravels towards the scene and is then reflected back by the surface tothe sensor receiver. The distance covered is now 2ρ at time T. Therelationship can be written as:

$p = \frac{c\tau}{2}$

Signal S_(E)(t) may be reflected back by the scene surface and travelsback towards a receiver (back to receiver) and written as:

S _(E)(t)=A _(E)[2πf _(mod) t]

Because of free-path propagation attenuation (proportional to the squareof the distance) and the non-instantaneous propagation of IR opticalsignals leading to a phase delay ΔØ. The Attenuated Amplitude ofreceived signal may be referred to as A_(R). The interfering radiationat IR wavelength of emitted signal reaching the receiver may be referredto as B_(R).

Waveforms may be extracted in relation to each chip (e.g., as extractedfrom the available pixels in image data for each channel ofinformation), and these waveforms may represent, for example,information extracted from histogram information, or from other imageinformation. For example, a Fourier transform may be conducted on theimage data separately from extracted histogram information. In someembodiments, a histogram and a Fourier transform are used incombination.

A best-matching waveform approach may be utilized to estimate whichcolor and/or markings are associated with a particular chip. Forexample, each chip may have a corresponding waveform (for each imagechannel) and these may be used, in some embodiments, in aggregate, toclassify the chips based on chip values. In some embodiments, where thedata is not sufficiently distinguishing between different chip values(e.g., poor lighting makes it difficult to distinguish between pink andred), the system may be adapted to provide a confidence score associatedwith how closely matched the waveform is with a reference template. Thisconfidence score, for example, may be used to modify sensorycharacteristics, lighting conditions, etc., so that the confidence scoremay be improved on future image processing. In some embodiments, theinterfaces provided to users may also utilize the confidence score inidentifying how strong of a match was determined between chip images andreference templates. The received signals 1602 and 1604 may be differentfor each type of chip, and the waveforms may be processed through aclassifier to determine a “best match”. As described in someembodiments, the confidence in determining a best match may be based on(1) how well matched the chip is to a reference waveform, and (2) howmuch the chip is able to distinguish between two different referencewaveforms. The confidence score may be used to activate triggers toimprove the confidence score, for example, by automatically activatingreference illumination or requesting additional images (e.g., moving thecamera to get more pixels due to an obstruction, lengthening a shutterspeed to remove effects of motion, temporarily allocating additionalprocessing power to remove noise artifacts).

FIG. 17 is an illustration of a schematic of a game monitoring server 20according to some embodiments.

Game monitoring server 20 is configured to collect game event dataincluding bet data and hand start data and hand stop data, which may bereferred to generally as hand event data. The hand event data may beused to determine a hand count (e.g., the number of hands played byvarious players) for a particular gaming table for a particular periodof time. Bet and hand count data may be associated with a time stamp(e.g., start time, stop time, current time) and table identifier. Thebet data may also be associated with a particular player (e.g. dealer,customer) and a player identifier may also be stored in the datastructure.

For simplicity, only one game monitoring server 20 is shown but systemmay include more game monitoring servers 20. The game monitoring server20 includes at least one processor, a data storage device (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface. Thecomputing device components may be connected in various ways includingdirectly coupled, indirectly coupled via a network, and distributed overa wide geographic area and connected via a network (which may bereferred to as “cloud computing”).

For example, and without limitation, the computing device may be aserver, network appliance, set-top box, embedded device, computerexpansion module, personal computer, laptop, or computing devicescapable of being configured to carry out the methods described herein.

As depicted, game monitoring server 20 includes at least one gameactivity processor 80, an interface API 84, memory 86, at least one I/Ointerface 88, and at least one network interface 82.

Game activity processor 80 processes the game activity data includingimage data, bet data, and so on, as described herein. Each processor 80may be, for example, a microprocessor or microcontroller, a digitalsignal processing (DSP) processor, an integrated circuit, a fieldprogrammable gate array (FPGA), a reconfigurable processor, aprogrammable read-only memory (PROM), or any combination thereof.

Memory 86 may include a suitable combination of computer memory that islocated either internally or externally such as, for example,random-access memory (RAM), read-only memory (ROM), compact discread-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like.

Each I/O interface 88 enables game activity processor 80 to interconnectwith one or more input devices, such as a keyboard, mouse, camera, touchscreen and a microphone, or with one or more output devices such as adisplay screen and a speaker.

Each network interface 82 enables game activity processor 80 tocommunicate with other components, to exchange data with othercomponents, to access and connect to network resources, to serveapplications, and perform other computing applications by connecting toa network (or multiple networks) capable of carrying data including theInternet, Ethernet, plain old telephone service (POTS) line, publicswitch telephone network (PSTN), integrated services digital network(ISDN), digital subscriber line (DSL), coaxial cable, fiber optics,satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network,fixed line, local area network, wide area network, and others, includingany combination of these.

Application programming interface (API) 84 is configured to connect withfront end interface 60 to provide interface services as describedherein.

Game activity processor 80 is operable to register and authenticate userand client devices (using a login, unique identifier, and password forexample) prior to providing access to applications, network resources,and data. Game activity processor 80 may serve one user/customer ormultiple users/customers.

FIG. 18 . illustrates a schematic of a bet recognition device 30according to some embodiments.

As depicted, bet recognition device 30 may include an imaging component90, sensor component 92, processor 91, memory 94, at least one I/Ointerface 96, and at least one network interface 98.

Processor 91 may be, for example, any type of general-purposemicroprocessor or microcontroller, a digital signal processing (DSP)processor, an integrated circuit, a field programmable gate array(FPGA), a reconfigurable processor, a programmable read-only memory(PROM), or any combination thereof.

Memory 94 may include a suitable combination of any type of computermemory that is located either internally or externally such as, forexample, random-access memory (RAM), read-only memory (ROM), compactdisc read-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like.

Each I/O interface 96 enables bet recognition device 30 to interconnectwith one or more input devices, such as a keyboard, mouse, camera, touchscreen and a microphone, or with one or more output devices such as adisplay screen and a speaker.

Each network interface 98 enables bet recognition device 30 tocommunicate with other components, to exchange data with othercomponents, to access and connect to network resources, to serveapplications, and perform other computing applications by connecting toa network.

Bet recognition device 30 may also include a scale component. Betrecognition device 30 may monitor chips and cards on the gaming tableusing scales. A scale may be placed underneath the casino table, orunderneath the area on which the chips or cards are placed. The scalemay take measurements during the time periods when no movement of thechips or cards is done. For example, a dealer may and the players mayplace the cards or chips on the table, upon seeing a particular gesture,a scale may read the weight and the system may determine, based on theweight, as well as the monitoring mechanism, the number of cards orchips on the table. The weight reading may be done at a later point, toconfirm that no cards or chips were taken off of the table. The scalemay take measurements of the weight responsive to a command by thesystem. As such, the system may determine when the chips or cards arenot touched by the dealer or the player, thereby ensuring that a correctmeasurement is taken and, in response to such a determination, sending acommand to measure the weight of the chips or cards. As an example,based on the weight and the coloring of the chips, the system maydetermine the present amount of the chips the user may have. This may bean example of game activity.

Using these techniques, the system may monitor and track not only thechips of the dealers but also the chips of the players, may track theprogress of each player, may be able to see when and how each player isperforming, and may also monitor new hands to determine hand count. Thesystem may therefore know the amount of chips gained or lost in realtime at any given time, and may also know the number of cards in eachplayer's hand, and so on.

As described herein, embodiments described herein may provide systems,methods and devices with bet recognition capabilities. Bet recognitiondata may be generated and collected as game event data and may beconnected to hand count data. For example, a hand may involve bettingchips and system may detect chips using bet recognition device 30.

The bet recognition device may capture image data for bet data inresponse to chip detection in a betting region.

The system may involve bet recognition cameras inside of a bumper of thegaming table on the dealer's side. The cameras may be in nearly the samelocation as this may simplify table retrofitting. All of componentsincluding computers for both bet recognition and hand count may beinstalled there.

FIGS. 19 to 39, 51 and 52 illustrate schematic diagrams of betrecognition devices and imaging components according to someembodiments.

Embodiments described herein may implement bet recognition devices andimaging components with different camera positioning options.

For example, a bet recognition device may have an imaging component withone wide-angle lens camera at the back of the table that scans theentire casino table. An example schematic 1900 is shown in FIG. 19including illustrative lines corresponding to a field of view for thecamera.

As another example, a bet recognition device may have an imagingcomponent with three cameras at the back of the table (e.g. left,middle, right), pointing outward towards the player positions.

An illustrative schematic of an imaging component 3200 and 3300 withthree cameras is shown in FIGS. 32 and 33 . An example table layout isshown in FIG. 28 . Example playing areas for image capture is shown inFIGS. 29 to 31 . For example, there may be 21 or 28 playing areas forimage capture for seven players with three or four playing areasallocated per player. The playing areas may be for bets, chips, cards,and other play related objects. As shown, fields of view for cameras mayoverlap such that one or more cameras may capture image datacorresponding to each play area. For example, two cameras may captureimage data corresponding to a play area. As another example, threecameras may capture image data corresponding to a play area.

As shown in FIGS. 21 to 27 a dealer may place cards on the table withinone or more fields of view of cameras to capture image data relating tocards, dealer card play, and dealer gestures. The image data captured bydifferent cameras with overlapping fields of view may be correlated toimprove gesture recognition, for example.

An example is shown in FIGS. 20 to 31 including illustrative linescorresponding to fields of view for the cameras. As shown in 2000 and2100, there may be overlapping fields of view between cameras. FIGS.24A-24D (shown in images 2400A-2400D illustrate a dealer at a tableundertaking motions to serve cards in relation to a card game. Thedealer's motions may temporarily obstruct various betting areas, and itmay be advantageous to have overlapping fields of view to account forsuch obstruction. In some embodiments, where there is a single camera,the shutter speed may be slowed so that the dealer's motions are removedduring processing. Similarly, FIGS. 25A-25E, and FIGS. 26 and 27 at2500A-2500E, 2600 and 2700 show alternate dealing motions.

FIGS. 28-31 illustrate how a computing system may track the variousbetting areas. As provided in the diagrams 2800, 2900, 3000, and 3100, abet recognition system may utilize imaging components and/or sensorsthat process images from the perspective of a dealer. As depicted, threedifferent cameras may be used, each tracking one or more differentbetting areas that are associated with each player. There may bemultiple betting areas that a player may be in (e.g., craps). Asindicated in FIG. 30 and FIG. 31 , the fields of view may overlap forthe cameras. The overlapping field of view may aid in increasing theconfidence score of a particular bet analysis.

As a further example, a bet recognition device may have an imagingcomponent with three cameras at the back of the table (e.g., left,middle, right), pointing inward. An example 3400 is shown in FIG. 34including illustrative lines corresponding to fields of view for thecameras.

As another example, a bet recognition device may have an imagingcomponent with three cameras in the middle of the table (left, middle,right), pointing outward. An example 3500 is shown in FIG. 35 includingillustrative lines corresponding to fields of view for the cameras.

As an additional example, a bet recognition device may have an imagingcomponent with two cameras at the back of the table (left and right),pointing inward. An example 3600 is shown in FIG. 36 includingillustrative lines corresponding to fields of view for the cameras.

As an additional example, a bet recognition device may have an imagingcomponent with seven endoscope cameras placed at the front of the table,between the players and the betting area, pointing inward. Examples 3700and 3900 are shown in FIGS. 37 and 39 . Example hardware components3802-3808 for implementing an example imaging component are shown inFIG. 38 . FIG. 39 illustrates an alternate embodiment 3900 whereincameras are positioned differently.

Endoscopes may be augmented by mirrors and light emitters in someexample embodiments. The use of endoscopes may be an effective methodfor achieving high accuracy when analyzing chips as the cameras arelocated closest to the betting area as possible with no obstructions.However, the effort required to retrofit existing casino tables withendoscopes may be arduous to be practical in some situations.Accordingly, different example camera implementations may be useddepending on the situation.

When capturing imaging data of the betting area for the purposes ofanalyzing the number and value of a player's chips, the cameras maycapture images using infrared (IR) technology which is not visible tothe human eye. The cameras may use IR emitters and receivers which canoperate on the same wavelength. Further, the cameras may be programmedto capture images at different times so that the wavelengths do notinterfere with one another, making sure that each image is notobstructed.

In another aspect, embodiments described herein may provide automaticcalculation of manual casino shoes and associated statistics including,for example, shuffle per hour.

The “casino shoe” is the card release mechanism on casino tables, whichmay contain several decks of cards. Dealers use the casino shoe tosource cards for dealing each hand.

A casino “shoe” may be monitored by hardware components to provide ameasure of how many shuffles occurred per hour, how many cards weredealt to players (including the dealer) per hour, and so on. Forexample, to count cards on a manual shoe, a magnet and a magnetic sensormay be attached to the shoe and used to trigger when the shoe is emptyof cards.

Alternatively, a dealer procedure could require the shoe to be turned onits side with the shoe weighted wedge removed. This can be recognizedwith the retrofitting of the tilt switch (i.e., single-axis gyroscope)inside or outside of the shoe. This may recognize when a shoe has beendepleted and must be refilled. The bet recognition device 20 can thuscollect data on cards such as, for example, how many shuffles occur perhour (which varies because shoes are depleted at different depths basedon different gameplay scenarios) and indicate when the bet recognitionprocess does not need to look for player bets.

Embodiments described herein automate the process of counting “shoe”related statistics (e.g., the number of shoes). Prior attempts mayrequire data to be collected manually by the pit manager

FIG. 40 illustrates that a shoe 4000 may be positioned at variousinclines (increasing, decreasing). FIGS. 41 to 43 illustrate differentviews 4100, 4200, 4300 of a card shoe which may be used in exampleembodiments. FIGS. 44 to 46C illustrate different example positions4400, 4500, 4600A-4600C of a card shoe on a gaming table according tosome example embodiments.

In another aspect, embodiments described herein may provide backgroundimage subtraction and depth segmentation. Background subtraction is acomputational technique used according to embodiments described hereinto differentiate the background image from the foreground image whencapturing image data of the casino table. Depth segmentation is anothertechnique that may achieve accuracy in analyzing a specific image dataof a three-dimensional area of the betting table. This technique allowsembodiments described herein to establish a region of analysis on thecasino table and to narrow the focus of the camera onto the bettingarea, without incorporating the chips not relating to that player's bet(e.g., in another betting area, or out of play altogether). This areamay be referred to as a “bounding box”.

This is one of different example ways to perform the removal of thebackground of an image when establishing a “bounding box” to identifythe number and types of chips in a betting area. Other exampletechniques include graph cut, frame differencing, mean filtering,Gaussian averaging, background mixture models, and so on.

After capturing two-dimensional images with a camera embedded in thecasino table, background subtraction can be run as an automaticalgorithm that first identifies the boundaries of object, and thenallows the software to separate that object (e.g., chips, hands, or anyother objects commonly found on the casino floor) from its background.

To increase the effectiveness of background subtraction, embodimentsdescribed herein may support three-dimensional background subtraction,making use of depth data to differentiate the foreground image from thebackground (illuminating the object with infrared and/or using lasers toscan the area).

Based on the distortion of the light, after projecting light onto theobject, embodiments described herein can determine how far away anobject is from the camera, which helps to differentiate the object fromits background in two-dimensional and three-dimensional images.

In a further aspect, embodiments described herein may count the numberof stacks of chips placed in the betting area.

Embodiments described herein may be used to administer casino standardsfor how players should place their stacks of chips. Known approachesrequire this to be checked manually by a dealer before a hand can begin.

Embodiments described herein may have the capacity to accurately collectdata on every hand by counting the chips and their value. Embodimentsdescribed herein may have the ability to establish the quality ofbetting by using depth cameras to capture errors made by players.

Embodiments described herein may scan and accurately count any type ofchips, including but not limited to four example varietals: no stripes,all different colors; stripes, but all the same on all chips (differentcolours); varying colors and stripes (stripes are smeared); anddifferent colors and stripes (stripes are well-defined). FIGS. 47 and 48show example views of chip stacks.

When a camera is elevated above the level of the table, embodimentsdescribed herein may be programmed to detect the top of the chip first.An example is shown in FIG. 49 . Once the system has recognized the topof the chip, the process can quickly count top-down to establish anaccurate chip count and then shift to scan bottom-up to identify thevalue of all these chips. This may be important due to several examplefeatures. Several cameras according to embodiments described herein maybe elevated above the tabletop (e.g., looking past the dealer's body,midway up the torso). This allows the system to recognize the bettingarea by establishing a “bounding box”, which is an area on the bettingtable that is scanned by to analyze physical objects, i.e., the chips.

In some casino games, such as baccarat, other chip stacks on the tablemay obstruct players' bets from the camera's view, so the process mayencounter obstruction. To solve this, cameras placed at elevated anglesmay allow embodiments described herein to see more of table and maintainhigh accuracy.

In another aspect, embodiments described herein may determine chip stacksequence in a betting area.

When bets are being placed in betting areas, embodiments describedherein may have the capacity to capture data on the visual sequence ofthe chips when players place them in the betting area.

Embodiments described herein may take pointed pictures (e.g. image data)of varying stripe patterns on a cylinder (e.g. the shape of the chipstack), which can vary in pixel number. In accordance with embodiments,to optimize the speed and accuracy of recognizing chips, the system maybenchmark the size of image captures at 3 by N pixels for height andwidth measurements, in some examples. Other benchmarks may be used.

Embodiments described herein may accurately recognize when a frequencysignature is present and also allows for the training of new stripepatterns, for example.

Embodiments described herein may recognize and confirm this pattern byusing a camera to see the sequence of the stripes, as well as the colorof the chips. The system may be configured to be precise enough toregister the specific hue of the chip, which is relevant for the systemin terms of both accuracy of results and security purposes.

The casino standard of chip stack sequence may typically have playersbet in a specific order: highest value chips on the bottom of the stack,with lower value chips layered upward. FIG. 50 shows a chip stack withthis example standard.

Beyond recognizing the typical pattern (e.g., highest value on bottom,lowest on top (see FIG. 50 ) embodiments described herein may useFourier analysis to find different types of chip, applying learnablefrequency/discrete signal patterns on the chip stack. Embodimentsdescribed herein may also combine with Neural Networks or equivalentlearning techniques to identify when the pattern is present. Bypreprogramming the process to recognize particular nodes of the neuralnetwork relating to chips, Embodiments described herein can alsodeactivate specific nodes so that they are not optional paths in thedecision process, which may improves both speed and accuracy.

This feature also enables embodiments described herein to “turn off”certain classes or types of chips during its analysis of stack sequence,so that it does not search for particularly values of chip. This has theeffect of increasing the speed of the ‘chip stack sequence’ processwhile maintaining accuracy.

By training multiple classifiers, embodiments described herein canautomatically remove the last highest chip value from each stacksequence to increase the speed of analysis. Each time the algorithmscans the chip stack, it may run the example sequence as follows: 12345(scanning all chips), 1234 (removing a value), 123 (removing anothervalue), 12 (etc.), 1 (etc.), and finally removing all chip classifiersduring its last scan. By removing values over multiple analyses,embodiments described herein may not look for certain values when theyare not present in a player's bet. This may enable the process toincrease its operation speed over time while maintaining accuracy.

Embodiments described herein may also recognize player errors on theoccasions when the stack sequence does not meet the casino standard.

If the player makes an error or a failure to meet the standards of thecasino, embodiments described herein may log that error and may generatean electronic alert for transmission to a casino manager to provide analert of the error. The automatic alert may depend on the severity ofthe error.

Embodiments described herein may also detect between different versionsof a chip using pattern recognition and signal processing techniques.For example, different colors and patterns on chips may have differentfrequencies of image data and which may be used to detect differentversions of chips.

Embodiments described herein may also detect when chips requirecleaning. With the same frequency image techniques used for detectingdifferent versions of the chip, embodiments described herein may alsodetect when dirt has built up on chips. The build-up of debris on thechips disrupts the frequency of its colour as captured by the camera.

Embodiments described herein may also use mirrors on casino tabletops aspart of the image capture component of bet recognition device 20.

Embodiments described herein may require two spaces on the surface topof a casino table to run at its top capacity.

To make these spaces smaller, one approach may be to use mirrors on thetable surface to reflect the camera illumination technology onto thechips, enabling embodiments described herein to effectivelydifferentiate the object from the background to enable the chip countingprocess, and other applications, to run at high capacity. Adding mirrorsto the tabletop can help improve aesthetics, enable easier retrofit, andmake embodiments described herein less obtrusive to the dealer andplayers.

Embodiments described herein may be described in relation to blackjackas an illustrative example. However, embodiments described herein mayanalyze other casino games.

For example, Baccarat is another game at the casino that the presentlydisclosed system can be used to analyze. Baccarat is a comparing cardgame played between two hands on the table, the player's and thebanker's. Each hand of baccarat has three possible outcomes: “player”win, “banker” win, and “tie”.

Embodiments described herein may obtain additional info about the handon the table, in addition to the bet information, such as what cards thedealer and player had in their hands. Embodiments described herein maydetermine what a player bets, and whether the player has won, lost, ortied.

By providing more accurate account of table dynamics, embodimentsdescribed herein may be essential for improving a casinos understandingof the process of how people are playing baccarat.

FIG. 53 is an example workflow 5300, illustrative of some embodiments.FIG. 53 is an example workflow 5300 illustrative of some embodiments.Workflow 5300 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible. 5300 is a process for extracting and saving bet data asobtained from image data. The recognition process 5302 and the featureextraction profess 5304 are provided as examples, illustrative ofexample steps that may be utilized to determine a chip count result.

FIG. 54 is an example workflow 5400 illustrative of some embodiments.Workflow 5400 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible. 5400 is a process for extracting and saving bet data asobtained from image data. At 5402, coordinates are obtained in relationto the bets as represented by chips in the betting areas. For example,coordinates may include chip height, chip width, and a horizon elementthat may be expressed in the form of pixels. The coordinates may beutilized to obtain lists of pixels for sampling from the images,generating a sample space using the list of pixels. Connected componentsare calculated from the sample space, and centroids are calculated foreach of the connected components. At 5404, coordinate information isextracted. Extraction may include creating headers for coordinateinformation feature, mapping sample coordinates into a region ofinterest space into an original color image space, and creating a listof mapped coordinates. At 5406, the system is configured to create anempty ground truth feature, which is a set of data values that arenormalized so that values can be compared against reference featurepoints. At 5408, features are extracted, and at 5410, coordinateinformation, ground truth and customized feature sets are concatenatedand converted to a dataframe at 5412. The customized feature sets arederived, for example, in relation to distinguishing features of chipmarkings that may vary between different facilities and the types ofchips being utilized.

FIG. 55 is an example workflow 5500 illustrative of some embodiments.Workflow 5500 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible. 5500 is a process for counting bet data as obtained from imagedata. At 5502, images are loaded from a data storage. During the imageloading step, color images may be obtained, and in some embodiments,depth and/or infrared imaging is obtained. A transformation matrix iscalculated, and the color image is read. At 5504, labels are loaded ontothe images (labels are originally blank, and will be updated when theregions of interest in the images are classified), and at 5506, featuresare extracted from the loaded images and labels. The feature extractionprocess, for example, may utilize a trained classifier wherein randomcolors may be assigned to classes to distinguish between differentclasses. During the feature extraction process, one or more lines (e.g.,vertical, horizontal, diagonal) are used for sampling pixels and/or dotrepresentations of the chips.

In some embodiments, the chip pixels themselves in a region of interestrepresenting a particular chip are blurred and/or otherwise aggregatedso that the sampled regions are more likely representative of the chipin its entirety. Such an approach may reduce the number of pixelsrequired for analysis and/or storage, increasing the speed andefficiency of classification. The dots and the pixels may thereforerepresent adjacent colours, and based on the height and distance, thenumber of chips can be determined, and each chip can be segregated bydividing the height of a stack by the height of a chip, creatingindividual chip segments. In some embodiments, the sample line is a 1Dline of color/histogram values, and in other embodiments, a long 2D linehaving a length and a width of values are extracted.

This approach may be helpful where gaming facilities release differentversions of chips, often differentiated by subtle differences in hue(e.g., changing the frequency of color as captured by the imagingcomponent), or in the sequence of markings, such as stripes.

Accordingly, in some embodiments, classification include normalizing thepixel values of an image capture, gamma-decoding the image such thatpixel values are proportional to the number of photos impacting thecamera sensor, combining the pixels in the height dimension into asingle 1D line, which is then truncated to form a uniform width for aFast Fourier Transform analysis. Such an approach may also includeclassification based, for example, at least on the magnitude of thecomplex sinusoids returned.

Colors, among other visual markers, may be mapped to various classes. At5508, chips are classified (e.g., based on machine-vision derivedestimations). The system may also be trained to differentiate betweennew versions of chips from obsolete versions of chips, which may beremoved from circulation to maintain security and consistency.

At 5510, chips are counted through based on the classifications, and at5512, chip counts are printed to a file (e.g. encapsulated and/orencoded for transmission to downstream systems).

FIG. 55 is an example workflow 5500 illustrative of some embodiments.Workflow 5500 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible.

At 5502, detecting, by an imaging component, that one or more chips havebeen placed in one or more defined bet areas on a gaming surface, eachchip of the one or more chips having one or more visual identifiersrepresentative of a face value associated with the chip, the one or morechips arranged in one or more stacks of chips.

At 5504, capturing, by the imaging component, image data correspondingto the one or more chips positioned on the gaming surface, the capturingtriggered by the detection that the one or more chips have been placedin the one or more defined bet areas.

At 5506, transforming, by an image processing engine, the image data togenerate a subset of the image data relating to the one or more stacksof chips, the subset of image data isolating images of the one or morestacks from the image data.

At 5508, recognizing, by an image recognizer engine, the one or morechips composing the one or more stacks, the recognizer engine generatingand associating metadata representative of (i) a timestamp correspondingto when the image data was obtained, (ii) one or more estimated positionvalues associated with the one or more chips, and (iii) one or more facevalues associated with the one or more chips based on the presence ofthe one or more visual identifiers.

At 5510 segmenting, by the image recognizer engine, the subset of imagedata and with the metadata representative of the one or more estimatedposition values with the one or more chips to generate one or moreprocessed image segments, each processed image segment corresponding toa chip of the one or more chips and including metadata indicative of anestimated face value and position.

At 5512, determining, by a game monitoring engine, one or more bet datavalues, each bet data value corresponding to a bet area of the one ormore defined bet areas, and determined using at least the number ofchips visible in each of the one or more bet areas extracted from theprocessed image segments and the metadata indicative of the face valueof the one or more chips.

The advantages of the some embodiments are further illustrated by thefollowing examples. The examples and their particular details set forthherein are presented for illustration only and should not be construedas limitations.

In implementation, the process of patching together images may beginwith capturing a particular number of samples from each camera that ismounted to the table.

Different scenarios of chips are used for each sample. These scenariosalso include extreme situations so that the machine can learn, whichallows it to handle simpler scenarios with a greater relative ease. Thecaptured samples are then labeled by denomination to create the filethat is used in training.

The capturing tools developed by the Applicant have been capable offocusing mainly on the bet area, while omitting any surroundingenvironments that might cause discrepancies. The removal of surroundingenvironments helps the system to ignore any background chips duringtraining and testing processes.

During testing, it was noted that the removal of background chipsimproved accuracy. In the process of training and testing, higheraccuracy of datasets and successful training were found throughcapturing and labelling samples in a brightly lit setting and testingthem in a dimly lit setting, or executing both processes in a brightlylit setting. This approach was found to produce a higher accuracy thanperforming both of the process in a dimly lit setting or performing thefirst process in a dimly lit setting while next in bright light.

Applicant also found that providing more light from the side helped thesystem identify colors better.

The embodiments of the devices, systems, and methods described hereinmay be implemented in a combination of both hardware and software. Theseembodiments may be implemented on programmable computers, each computerincluding at least one processor, a data storage system (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions describedherein and to generate output information. The output information isapplied to one or more output devices. In some embodiments, thecommunication interface may be a network communication interface. Inembodiments in which elements may be combined, the communicationinterface may be a software communication interface, such as those forinter-process communication. In still other embodiments, there may be acombination of communication interfaces implemented as hardware,software, and combination thereof.

Throughout the foregoing discussion, numerous references will be maderegarding servers, services, interfaces, portals, platforms, or othersystems formed from computing devices. It should be appreciated that theuse of such terms is deemed to represent one or more computing deviceshaving at least one processor configured to execute softwareinstructions stored on a computer readable tangible, non-transitorymedium. For example, a server can include one or more computersoperating as a web server, database server, or other type of computerserver in a manner to fulfill described roles, responsibilities, orfunctions.

The discussion provides many example embodiments. Although eachembodiment represents a single combination of inventive elements, otherexamples may include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, other remainingcombinations of A, B, C, or D, may also be used.

The term “connected” or “coupled to” may include both direct coupling(in which two elements that are coupled to each other contact eachother) and indirect coupling (in which at least one additional elementis located between the two elements).

The technical solution of embodiments may be in the form of a softwareproduct. The software product may be stored in a non-volatile ornon-transitory storage medium, which can be a compact disk read-onlymemory (CD-ROM), a USB flash disk, or a removable hard disk. Thesoftware product includes a number of instructions that enable acomputer device (personal computer, server, or network device) toexecute the methods provided by the embodiments.

The embodiments described herein are implemented by physical computerhardware, including computing devices, servers, receivers, transmitters,processors, memory, displays, and networks. The embodiments describedherein provide useful physical machines and particularly configuredcomputer hardware arrangements. The embodiments described herein aredirected to electronic machines and methods implemented by electronicmachines adapted for processing and transforming electromagnetic signalswhich represent various types of information. The embodiments describedherein pervasively and integrally relate to machines, and their uses;and the embodiments described herein have no meaning or practicalapplicability outside their use with computer hardware, machines, andvarious hardware components. Substituting the physical hardwareparticularly configured to implement various acts for non-physicalhardware, using mental steps for example, may substantially affect theway the embodiments work. Such computer hardware limitations are clearlyessential elements of the embodiments described herein, and they cannotbe omitted or substituted for mental means without having a materialeffect on the operation and structure of the embodiments describedherein. The computer hardware is essential to implement the variousembodiments described herein and is not merely used to perform stepsexpeditiously and in an efficient manner.

Although the embodiments have been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade herein.

Moreover, the scope of some embodiments is not intended to be limited tothe particular embodiments of the process, machine, manufacture,composition of matter, means, methods and steps described in thespecification. As one of ordinary skill in the art will readilyappreciate from some embodiments, processes, machines, manufacture,compositions of matter, means, methods, or steps, presently existing orlater to be developed, that perform substantially the same function orachieve substantially the same result as the corresponding embodimentsdescribed herein may be utilized. Accordingly, embodiments are intendedto include within their scope such processes, machines, manufacture,compositions of matter, means, methods, or steps.

As can be understood, the examples described above and illustrated areintended to be exemplary only.

What is claimed is:
 1. A system for monitoring game activities at agaming table having at least one betting area, the system comprising:one or more client hardware devices positioned to capture image data ofone or more chips within the at least one betting area of the gamingtable from respective positions; a communication link configured fortransmitting the captured image data; and a processor configured to:retrieve captured image data from the one or more client hardwaredevices via the communication link; process the captured image data toidentify a chip stack region having at least one of the one or morechips; process the captured image data to determine one or more depthvalues corresponding to one or more distances from a reference point toone or more of the one or more chips; detect one or more specific chipsof the one or more chips within the chip stack region by using the oneor more depth values, and by using the one or more depth values toidentify the one or more chips outside the chip stack region; for eachrespective detected specific chip: generate one or more histograms ofthe respective detected one or more specific chips; estimate a chipvalue of the respective detected one or more specific chips as areference chip value associated with a reference histogram having thegreatest similarity to the one or more histograms of the respectivedetected one or more specific chips, the reference histogram being froma library of reference histograms; and generate an output data structurestoring one or more data fields representative of the chip value of eachof the detected one or more specific chips; and transmit the output datastructure and a control command to a front end interface device fordisplaying the one or more data fields representative of the chip valueof each of the detected one or more specific chips; wherein the one ormore histograms are developed as part of a calibration process usingreal-world images of chips to train a base set of features for a machinelearning process to dynamically associate and create linkages as newchip types are introduced; and wherein the estimation of the chip valuebased on the reference histogram is based at least on the trained set offeatures.
 2. The system of claim 1, wherein the processor is configuredto generate the one or more histograms by: performing a Fouriertransformation on the captured image data to obtain one or more plotsdecomposing the captured image data into a series of periodic waveformswhich in aggregation form the respective histogram.
 3. The system ofclaim 2, wherein the processor estimating the chip value of therespective detected one or more specific chips comprises: comparing eachwaveform of the series of periodic waveforms against a library ofreference waveforms to estimate the chip value of the respectivedetected one or more specific chips through identifying the referencewaveform that has the greatest similarity to the respective waveform. 4.The system of claim 1, wherein the processor is further configured to:pre-process the captured image data to filter out at least a portion ofbackground image data and generate a compressed image data of the one ormore chips free of the background image data by using an estimated chipstack height and the one or more depth values to determine a chip stackbounding box for differentiating between the background image data andimage data representative of the one or more specific chips.
 5. Thesystem of claim 1, further comprising an illumination light emitteradapted to provide a reference illumination on the one or more chips,the illumination light emitter positioned at a substantially horizontalangle to provide illumination on one or more sides of the one or morechips; the substantially horizontal angle selected such that thepresence of shadows on the one or more chips is reduced.
 6. The systemof claim 1, wherein the one or more depth values are determined bymeasuring stereo parallax, shadow measurements, light intensitymeasurements, relative size measurements, and illumination gridmeasurements.
 7. The system of claim 1, wherein: at least one of the twoor more client hardware devices are positioned to capture the image dataat an offset angle relative to a plane of the at least one betting areaof the gaming table; and wherein the offset angle permits the at leastone of the two or more client hardware devices to capture the image datafrom the one or more sides of the one or more chips.
 8. The system ofclaim 1, wherein the processor is configured to retrieve captured imagedata from the two or more client hardware devices via the communicationlink in response to activation events.
 9. The system of claim 1, whereinthe processor is further configured to: associate the output datastructure to one or more players interacting with the monitored gameactivities being played on the gaming surface; and aggregate the outputdata structures associated with the one or more players for the durationof the monitored game activities.
 10. The system of claim 1, wherein thecalibration process using the real-world images of chips to train thebase set of features allows for flexible adaptation to differentenvironments.
 11. A method for monitoring game activities at a gamingtable having at least one betting area, the method comprising:retrieving captured image data from one or more client hardware devicespositioned to capture image data of one or more chips within the atleast one betting area of the gaming table from respective positions viaa communication link; processing the captured image data to identify achip stack region having at least one of the one or more chips;processing the captured image data to determine one or more depth valuescorresponding to one or more distances from a reference point to one ormore of the one or more chips; detecting one or more specific chips ofthe one or more chips within the chip stack region by using the one ormore depth values to represent each specific chip, and by using the oneor more depth values to identify the one or more chips outside the chipstack region; for each respective detected specific chip: generating oneor more histograms of the respective detected one or more specificchips; estimating a chip value of the respective detected one or morespecific chips as a reference chip value associated with a referencehistogram having the greatest similarity to the one or more histogramsof the respective detected one or more specific chips, the referencehistogram being from a library of reference histograms; and generatingan output data structure storing one or more data fields representativeof the chip value of each of the detected one or more specific chips;and transmitting the output data structure and a control command to afront end interface device for displaying the one or more data fieldsrepresentative of the chip value of each of the detected one or morespecific chips; wherein the one or more histograms are developed as partof a calibration process using real-world images of chips to train abase set of features for a machine learning process to dynamicallyassociate and create linkages as new chip types are introduced; andwherein the estimation of the chip value based on the referencehistogram is based at least on the trained set of features.
 12. Themethod of claim 11, wherein generating the one or more histogramscomprises: performing a Fourier transformation on the captured imagedata to obtain one or more plots decomposing the captured image datainto a series of periodic waveforms which in aggregation form therespective histogram.
 13. The method of claim 12, wherein estimating thechip value of the respective detected one or more specific chipscomprises: comparing each waveform of the series of periodic waveformsagainst a library of reference waveforms to estimate the chip value ofthe respective detected one or more specific chips through identifyingthe reference waveform that has the greatest similarity to therespective waveform.
 14. The method of claim 11, further comprising:pre-processing the captured image data to filter out at least a portionof background image data and generate a compressed image data of the oneor more chips free of the background image data by using an estimatedchip stack height and the one or more depth values to determine a chipstack bounding box for differentiating between the background image dataand image data representative of the one or more specific chips.
 15. Themethod of claim 11, further comprising: determining a presence of one ormore obstructing objects that are partially or fully obstructing the oneor more chips from being imaged by the client hardware device, thepresence of the one or more obstructing objects being determined bycontinuously monitoring the one or more depth values to track when theone or more depth values abruptly changes responsive to the obstruction.16. The method of claim 11, wherein the one or more depth values aredetermined by measuring stereo parallax, shadow measurements, lightintensity measurements, relative size measurements, and illuminationgrid measurements.
 17. The method of claim 11, wherein: the capturedimage data from the at least one of the one or more client hardwaredevices includes image data from respective positions at an offset anglerelative to a plane of the at least one betting area of the gamingtable; and wherein the captured image data including image data fromrespective positions at an offset angle relative to the plane includesimage data from the one or more sides of the one or more chips.
 18. Themethod of claim 11, further comprising retrieving captured image datafrom the one or more client hardware devices via the communication linkin response to activation events.
 19. The method of claim 11, whereinthe calibration process using the real-world images of chips to trainthe base set of features allows for flexible adaptation to differentenvironments.
 20. A non-transitory computer readable medium storingmachine-interpretable instructions, which when executed by a processor,cause the processor to perform a method for monitoring game activitiesat a gaming table having at least one betting area, the methodcomprising: retrieving captured image data from one or more clienthardware devices positioned to capture image data of one or more chipswithin the at least one betting area of the gaming table from respectivepositions via a communication link; processing the captured image datato identify a chip stack region having at least one of the one or morechips; processing the captured image data to determine one or more depthvalues corresponding to one or more distances from a reference point toone or more of the one or more chips; detecting one or more specificchips of the one or more chips within the chip stack region by using theone or more depth values to represent each specific chip, and by usingthe one or more depth values to identify the one or more chips outsidethe chip stack region; for each respective detected specific chip:generating one or more histograms of the respective detected one or morespecific chips; estimating a chip value of the respective detected oneor more specific chips as a reference chip value associated with areference histogram having the greatest similarity to the one or morehistograms of the respective detected one or more specific chips, thereference histogram being from a library of reference histograms; andgenerating an output data structure storing one or more data fieldsrepresentative of the chip value of each of the detected one or morespecific chips; and transmitting the output data structure and a controlcommand to a front end interface device for displaying the one or moredata fields representative of the chip value of each of the detected oneor more specific chips; wherein the one or more histograms are developedas part of a calibration process using real-world images of chips totrain a base set of features for a machine learning process todynamically associate and create linkages as new chip types areintroduced; and wherein the estimation of the chip value based on thereference histogram is based at least on the trained set of features.